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The requirement for post-processing methods in 3D printing has increased due to its major limitations such as poor mechanical and surface properties. Thermal annealing, a heat-treatment procedure, has proven to be an excellent approach for increasing the mechanical strength of additive manufactured thermoplastics. The optimized thermal annealing parameters for maximum tensile strength are identified. The Adaptive Neuro-fuzzy Inference System (ANFIS) methodology is employed in this study to forecast the tensile strength of thermal annealed fused filament fabricated polylactic acid (PLA), carbon fiber filled polylactic acid (PLA-CF), and polycarbonate acrylonitrile butadiene styrene (PC-ABS). The root mean square error (RMSE) of the predicted tensile strength of PLA, PLA-CF, and PC-ABS are obtained as 1.012, 0.5, and 0.835 respectively. The coefficient of determination for the predicted model of thermoplastics PLA, PLA-CF, and PC-ABS is determined as 0.98, 0.99, and 0.89 respectively. The ANFIS model developed is successful in determining the tensile strength of annealed 3D printed thermoplastics at various annealing conditions of temperature and duration. Further, the study investigates the effect of heat treatment on the compressive, impact, and flexural properties of PLA prints. [ABSTRACT FROM AUTHOR]

The performance improvement of Automatic Generation Control (AGC) in a Multi Area Solar Thermal Power System (MASTPS) under a deregulated environment is considered as MIPS and their performance is compared using various controllers to improve the performance of AGC in the proposed MIPS. This study takes into account a three-area conventional thermal power system in conjunction with a solar thermal power plant in each area, along with the proper generation rate constraints (GRC). The performance of the different controllers, including the Adaptive Neuro Fuzzy Inference System (ANFIS) controller and the Droop Controller (DC), Fuzzy Logic Controller (FLC), Artificial Neural Network (ANN), and Fractional Order PID (FOPID) controllers, is compared in the proposed AGC deregulated power system. The proposed MIPS is designed and implemented to make the system as realistic as possible with real world scenarios. The recommended MIPS is being simulated in MALTAB Simulink environment and simulated for multiple times to investigate the performance improvement in AGC using the adopted controllers with 0.1 per unit of step load perturbation to verify its dynamic characteristics. According to the investigation, the suggested ANFIS controller improves the AGC performance in proposed MIPS and offers better dynamic performance than other controllers. [ABSTRACT FROM AUTHOR]

Inconel 718 is a superalloy with a high nickel content that is widely used in applications requiring solid mechanical behavior and resistance to oxidation and corrosion at high temperatures. This alloy has numerous applications in manufacturing steam turbine and jet aircraft interiors, aviation sector manifolds, and rotary spindles. It can be classified as a difficult-to-cut material unsuitable for traditional machining. The purpose of this paper is to develop prediction models for a wire electrical discharge machining (WEDM) process using response surface methodology (RSM), artificial neural networks (ANN), and adaptive neuro-fuzzy inference systems (ANFIS) and to determine which model is better at making accurate predictions. Pulse_ontime, pulse_offtime, servo voltage, flushing pressure, and wire feed were considered the main factors affecting volumetric material removal rate (VMRR) and arithmetic surface roughness (Ra), which were evaluated as WEDM performance characteristics. I-optimal design made with a computer algorithm was employed to develop experimental models. The results reveal that the wire feed and pulse_ontime were the most vital factors influencing VMRR, respectively, and the most significant factor influencing Ra is the pulse_ontime. The total percentage error of the three models demonstrated that the ANN and ANFIS models are more reliable and accurate than the RSM mathematical model. Finally, multiobjective optimization using the Pareto search algorithm was used to optimize mathematical, ANN, and ANFIS models to determine the optimum WEDM process parameters for machining Inconel 718 superalloy. [ABSTRACT FROM AUTHOR]

Parameters related to earthquake origins can be broken down into two broad classes: source location and source dimension. Scientists use distance curves versus average slowness to approximate the epicentre of an earthquake. The shape of curves is the complex function to the epicentral distance, the geological structures of Earth, and the path taken by seismic waves. Brune's model for source is fitted to the measured seismic wave's displacement spectrum in order to estimate the source's size by optimising spectral parameters. The use of ANFIS to determine earthquake magnitude has the potential to significantly alter the playing field. ANFIS can learn like a person using only the data that has already been collected, which improves predictions without requiring elaborate infrastructure. For this investigation's FIS development, we used a machine with Python 3x running on a core i5 from the 11th generation and an NVIDIA GEFORCE RTX 3050ti GPU processor. Moreover, the research demonstrates that presuming a large number of inputs to the membership function is not necessarily the best option. The quality of inferences generated from data might vary greatly depending on how that data is organised. Subtractive clustering, which does not necessitate any type of normalisation, can be used for prediction of earthquakes magnitude with a high degree of accuracy. This study has the potential to improve our ability to foresee quakes larger than magnitude 5. A solution is not promised to the practitioner, but the research is expected to lead in the right direction. Using Brune's source model and high cut-off frequency factor, this article suggests using machine learning techniques and a Brune Based Application (BBA) in Python. Application accept input in the Sesame American Standard Code for Information Interchange Format (SAF). An application calculates the spectral level of low frequency displacement (Ω0), the corner frequency at which spectrum decays with a rate of 2(fc), the cut-off frequency at which spectrum again decays (fmax), and the rate of decay above fmax on its own (N). Seismic moment, stress drop, source dimension, etc. have all been estimated using spectral characteristics, and scaling laws. As with the maximum frequency, fmax, its origin can be determined through careful experimentation and study. At some sites, the moment magnitude was 4.7 0.09, and the seismic moment was in the order of (107 0.19) 1023. (dyne.cm). The stress reduction is 76.3 11.5 (bars) and the source-radius is (850.0 38.0) (m). The ANFIS method predicted pretty accurately as the residuals were distributed uniformly near to the centrelines. The ANFIS approach made fairly accurate predictions, as evidenced by the fact that the residuals were distributed consistently close to the centerlines. The R2, RMSE, and MAE indices demonstrate that the ANFIS accuracy level is superior to that of the ANN. [ABSTRACT FROM AUTHOR]

Agriculture Industry is highly dependent on environmental and weather conditions. Many times, crops are spoiled because of sudden changes in weather. Therefore, we need a decision model to take care the water requirement of sensitive crops of agriculture industry. The proposed work presents a novel and proficient hybrid model for sensitive crop irrigation system (SCIS). For implementation of the model, brassica crop is taken. The duration and amount of water to be supplied is based upon the weather prediction and soil condition information. The decision model is developed using adaptive neuro‐fuzzy inference system (ANFIS) and artificial neural network (ANN) for brassica crops. In this model, if the input data values are available in range, then ANFIS model would be preferred and if the data sets are available for training, testing and validation then ANN model would be the best choice. The soil moisture, soil status in terms of temperature and leaf wetness are the input and flow control of sprinklers is the out for SCIS. The predicted outputs are analysed to assert the suitability of the proposed approach in the brassica crops. The proposed SCIS achieved an accuracy of 91% and 99% for ANFIS and ANN models respectively. [ABSTRACT FROM AUTHOR]

• Adaptive protection scheme for AC microgrid (MG) is proposed based on ANN-ANFIS model. • Different short-circuit levels, dynamic distribution system parameters, and diverse network topologies in MG operations have been addressed. • Effect of signal noise on protection system accuracy has been investigated. The increased penetration of distributed generation (DG) in distribution networks is altering the conventional mode in which the power grid's functions. Therefore, microgrids (MG) are most efficient and feasible solution to integrate sustainable energy sources into power grids. Existing protection method uses numerical relays. Thus, it provides flexibility to upgrade to a more sophisticated artificial intelligence (AI) based protection strategy. The use of signal processing (SP) and AI techniques, such as artificial neural networks (ANN), could assist in decision-making process resulting in successful isolation of faults. In this paper, an intelligent adaptive protection method is proposed for AC-MGs. This study showcases the potential of ANNs and SP techniques, complemented by adaptive neuro-fuzzy inference systems (ANFIS), in MG protection. These approaches demonstrate the utility of proposed methodology in design, testing, and evaluation of protection strategies, ultimately bolstering system security and efficiency. The proposed approach encompasses fault detection, classification, and location based on signal analysis, assisted by ANN and ANFIS. The methodology accounts for dynamic behavior and different faults, even in two distinct operating scenarios— grid-connected and islanded modes. Through comprehensive validation, results affirm the efficacy of proposed methodology, encouraging deployment of AI-based protection strategies for AC-MGs leading to the foundation for more robust and intelligent protection system. [Display omitted] [ABSTRACT FROM AUTHOR]

In Cognitive Radio Network, after sensing process, the selection and decision for a reliable channel from the list of free channels is important for assignment to Cognitive Users (CUs) for communication with Quality of Service (QoS). In this paper a consistent spectrum selection and decision scheme with two-fold neural network has been proposed for selection and decision process and its performance is compared with the schemes of Genetic algorithm and Back Propagation Neural Network (BPNN). BPNN- Adaptive Neuro Fuzzy Inference System (ANFIS) is a two-fold spectrum selection and decision approach which combines both BPNN and ANFIS techniques. A channel with the required QoS is selected based on the parameters such as Primary User (PU) states, signal strength, spectrum demand, velocity and distance. The simulation analysis shows that the BPNN–ANFIS technique reduces probability of blocking and dropping and therefore the accuracy of reliable channel selection obtained for the CUs use is more than 92%. The blocking probability of the proposed technique ranges from 1 to 3% which is much lower than the Genetic Algorithm (9–50%) and BPNN (8–40%). The maximum dropping probability of the proposed technique is only 4% and this is lower compared to 20% dropping in the other two techniques. [ABSTRACT FROM AUTHOR]

The present study aims to provide a suitable approach to optimize transmission line towers with size, shape and panel design variables. MSTOWER software was used for modeling, analysis and design of the transmission tower. The design requirements applied to the structure were in accordance with ASCE10-97 standard. Transmission line towers are optimized in two ways. The first method combines Biogeography based Optimization (BBO) algorithm with MSTOWER software called BBO-MSTOWER and the second method, called BBO-ANFIS, uses an adaptive fuzzy neural inference system (ANFIS). To analyze the structure in the optimization process with the aim of reducing the computations and time of the optimization process. In order to evaluate the proposed method, two full-scale transmission towers were optimized as numerical models using the above two methods. Finally, the optimal design and time required in the two optimization methods were compared with each other as well as with the initial design of the tower and the results reported in previous studies. The results showed that with acceptable accuracy, ANFIS resulted in a significant reduction in the calculation time of the entire optimization process. [ABSTRACT FROM AUTHOR]

Grid integrated solar photovoltaic (PV) power-generation conversion system (SPCS) with ancillary services such as power quality enhancement, real power harnessing, rapid power generation, and high conversion efficiency is the requirement for sustainable electric grid. Therefore, a novel Z-source DC–DC converter architecture is proposed, which has high gain and increased conversion efficiency for low-voltage PV applications. The proposed DC–DC converter is connected at the first stage, which provides very high gain at low-value duty cycle and offers very good efficiency. The characteristics of the solar PV strings exhibit many peaks when exposed to nonuniform irradiation making it difficult to track the global peak power. To overcome this problem, an adaptive neuro-fuzzy inference system (ANFIS) and Particle Swarm Optimization (PSO) based maximum power point tracking (MPPT) algorithm is proposed. This algorithm offers an extremely fast dynamic response with high accuracy and low reduced steady-state oscillations in contrast to conventional PSO based MPPT system. Further, the proposed system integrated with grid using synchronous reference frame (SRF) control in conjunction with ANFIS-PSO MPPT, which improves the grid-current profile under fixed and variable solar irradiation plus unbalanced nonlinear loading conditions. For performing various experimental investigations, DSP-dSPACE 1104 controller is used for realizing the inverter SRF-controller and ANFIS-PSO MPPT algorithm in MATLAB®\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$^{\circledR }$$\end{document}/SIMULINK environment. Experimental results shows that the proposed control structure improves the system’s dynamic performance under variable solar irradiation and frequent load changing conditions and adequately meets the requirement of the IEEE 519-2014 and IEEE 1547-2003 standards for grid connections. [ABSTRACT FROM AUTHOR]

Intermittency in wind offers the major challenge in accomplishing the wind energy as a dependable sustainable energy resource in power grid. Fluctuations in wind speed occur seasonally over a year and if this seasonality is considered, the prediction of the speed of wind can be made more accurate. In this paper, an attempt is made to apply a signal decomposition technique called Variational Mode Decomposition (VMD), which decomposes series of wind speed data into several intrinsic mode functions (IMFs) to make the data more regular thereby enhancing the accuracy of the wind speed forecast model. Then, artificial intelligence technique, Adaptive neuro fuzzy inference system (ANFIS) is applied for the wind speed prediction by combining the obtained modes from VMD. Here, wind data of two sites in India, Jogimatti and Lamba are taken for the study. Each site data is grouped into high and low wind speed months and later, this series is decomposed into regular modes using VMD. Later, ANFIS is applied for training and predicting the wind speed for different time horizons. [ABSTRACT FROM AUTHOR]

A modified firefly algorithm (MFO)-based adaptive neuro-fuzzy inference system (ANFIS) combined with the perturbation and observation (P&O) is used in this paper to track the maximum power point (MPP) in photovoltaic systems (PVs). The proposed method identifies and tracks the MPP in two stages. First, according to the irradiance on the solar panels, the ANFIS approximately identifies the MPP. In the second stage, the P&O method starts to act in the tracking cycle and initiates an accurate searching process from that point. The suggested hybrid method covers the problems of commonly-used methods, such as inability in detecting the global MPP under partial shading conditions (PSCs) and trapping in the local optima. Furthermore, the method provides significantly higher speed for the MPP tracking under various irradiance patterns. To prove the above-mentioned claims, the given approach is compared with the P&O method as a common method in the MPPT and particle swarm optimisation (PSO) which operates based on swarm intelligence. Simulation results obtained from MATLAB/Simulink environment show that the proposed method identifies and tracks the MPP under uniform irradiance and PSCs in a very short time of roughly 0.2 s. [ABSTRACT FROM AUTHOR]

Trail making test is a cognitive impairment test used for understanding the visual attention during the visual search task. The classical paper pencil method measures the completion time of the participant and there was no mechanism for comparison across the participant with similar feature. The psychologist has to observe the reactions of the participants during the trial process and there is no mechanism to capture it. This study made an attempt to resolve the above problem and tried to infer additional parameters which can support psychologist to understand the participant performance in trail making test. The insight provided by the approach is to extract various features which helps a psychologist by providing individual profiling and group profiling of a person and can understand the group of people who show similar cognitive impairment while performing trail Making Test. The proposed Intelligent Gaze Tracking approach could classify the participant into three different groups like low, high and medium cognitive impairment based on the extracted gaze features. The proposed approach has been compared across existing literature survey to significantly show the advantage of the system in terms of identifying the people with similar characteristics in terms of cognitive impairment. [ABSTRACT FROM AUTHOR]

Pim kinase enzyme has an essential role in the treatment of prostate, colon and acute myeloid leukaemia cancers. The indoles inhibitors were docked in the enzyme's active pocket in order to survey the inhibition mechanism and extract the ligands' conformations. The docking outcome shows that the active inhibitors have strong van der Waals interactions with residues of Ile185, Leu44, Leu120 and Leu174, hydrogen bonds with residues of Asp128, Arg122 and Glu171 and π-π interaction with the residue of Phe49. The sum of these interactions is ~80 kcal mol−1 contributing ~90% of total binding free energies. Using docking-based molecular descriptors, the unsupervised and supervised classifications were successfully carried out with the accuracy of 0.82 and 0.95, respectively, to categorize the active/inactive Pim kinase inhibitors. The vigorous quantitative assessment was performed using different machine learning techniques. The constructed QSAR model [(r2cal, r2p, r2m and Q2LOO) > 0.80 and (SEcal, SEp and SELOO) < 0.22] indicates that the molecular descriptors of nN, RDF20v and E1v can describe both the inhibition activities and the inhibition mechanism. The adequate evaluations of the molecular docking, classifications and QSAR analysis show that the current approaches can be used as valuable tools to design more effective new Pim kinase inhibitors for cancer treatment. [ABSTRACT FROM AUTHOR]

Infiltration plays a fundamental role in streamflow, groundwater recharge, subsurface flow, and surface and subsurface water quality and quantity. In this study, adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and random forest (RF) models were used to determine cumulative infiltration and infiltration rate in arid areas in Iran. The input data were sand, clay, silt, density of soil and soil moisture, while the output data were cumulative infiltration and infiltration rate, the latter measured using a double-ring infiltrometer at 16 locations. The results show that SVM with radial basis kernel function better estimated cumulative infiltration (RMSE = 0.2791 cm) compared to the other models. Also, SVM with M4 radial basis kernel function better estimated the infiltration rate (RMSE = 0.0633 cm/h) than the ANFIS and RF models. Thus, SVM was found to be the most suitable model for modelling infiltration in the study area. [ABSTRACT FROM AUTHOR]

• A newly developed hybrid intelligent model is proposed for soil moisture prediction. • The proposed model is validated against well-established machine learning models. • Several related morphological variables are used to build the predictive models. • The designed predictive models are evaluated and assessed comprehensively. • The ANFIS-GWO is showed a robust and reliable model for soil moisture prediction. Accurate estimation of soil moisture content is necessary for optimal management of water and soil resources. Soil moisture is an important variable in the hydrologic cycle, which plays an important role in the global water and energy balance due to its impact on hydrological, ecological, and meteorological processes. The purpose of the present study was to explore a newly developed hybrid intelligent model for simulating soil moisture content. A hybrid adaptive neuro fuzzy inference system (ANFIS) model-grey wolf optimization (GWO) algorithm was designed here and validated against the neural network (ANN), support vector regression (SVR) and standalone ANFIS models. The models input parameters were di-electric constant, soil bulk density, clay content and organic matter of 1155 soil samples. Various statistical indices were employed to evaluate the performances of the applied models. For a reliable ranking of models, the Global Performance Indicator (GPI) was utilized, which is a 5-agent index. The results evidenced the feasibility of the developed hybrid ANFIS-GWO model with superior simulation results. At the testing stage, the MAE and SI values for the ANFIS-GWO were 1.468% and 0.098, respectively, which indicated the superiority of the ANFIS-GWO compared to the ANFIS-Fuzzy C mean (MAE = 6.427%, SI = 0.354), and ANFIS-sub clustering (MAE = 2.137%, SI = 0.141) models. Based on the GPI, the ANFIS-GWO model was ranked as the best model, followed by the standalone ANFIS and SVR models, while the worst accuracy was attained through ANN model. The ANFIS-GWO model improved the simulation accuracy by 48% and 50%, respectively, compared to the standalone ANFIS and SVR models. In addition, based on the GPI, the ANFIS-GWO model presented an enhancement of about 77 percent compared to the ANN model. The high accuracy of the ANFIS-GWO model compared to the standalone ANFIS model represents the performance of the GWO algorithm for escaping local optima, which makes the ANFIS-GWO as a powerful tool for estimating soil moisture. Overall, the explored hybrid intelligent models demonstrated a reliable pedotransfer function of soil moisture where it can contribute to several geo-sciences engineering principles. [ABSTRACT FROM AUTHOR]

The requirement for post-processing methods in 3D printing has increased due to its major limitations such as poor mechanical and surface properties. Thermal annealing, a heat-treatment procedure, has proven to be an excellent approach for increasing the mechanical strength of additive manufactured thermoplastics. The optimized thermal annealing parameters for maximum tensile strength are identified. The Adaptive Neuro-fuzzy Inference System (ANFIS) methodology is employed in this study to forecast the tensile strength of thermal annealed fused filament fabricated polylactic acid (PLA), carbon fiber filled polylactic acid (PLA-CF), and polycarbonate acrylonitrile butadiene styrene (PC-ABS). The root mean square error (RMSE) of the predicted tensile strength of PLA, PLA-CF, and PC-ABS are obtained as 1.012, 0.5, and 0.835 respectively. The coefficient of determination for the predicted model of thermoplastics PLA, PLA-CF, and PC-ABS is determined as 0.98, 0.99, and 0.89 respectively. The ANFIS model developed is successful in determining the tensile strength of annealed 3D printed thermoplastics at various annealing conditions of temperature and duration. Further, the study investigates the effect of heat treatment on the compressive, impact, and flexural properties of PLA prints. [ABSTRACT FROM AUTHOR]

A metering roundabout where traffic is controlled by signals where phase times are influenced by queue detector occupancy may be the solution to reduce queue lengths under unbalanced traffic flows. In the past decades, a number of studies have attempted to evaluate the effectiveness of metering roundabout, especially on the dominant approach. Little studies, however, have been directed on prediction of the queuing lengths, which is essential to determine the detector locations. This paper introduces a queue length estimation model using adaptive neuro fuzzy inference system for unbalanced roundabout traffic flows. The adaptive neuro fuzzy inference system model consists of an input layer representing four parameters as arrival volumes, conflicting volumes, phase green and red time, and output layer with four neuron representing queuing length. MATLAB software and additional statistical tests were used as the tool to develop the models for the data. In order to conduct credible model validations, model output data were compared against the observed data collected using drones. The results from the analysis demonstrated that adaptive neuro fuzzy inference system model is able to estimate the queuing length at metering roundabouts. Thus, it is expected that the adaptive neuro fuzzy inference system model will help practitioners in determining optimal detector locations and will be a foundation research for roundabouts with signals. [ABSTRACT FROM AUTHOR]

Abstract Concrete corrosion is one of the most significant failure mechanisms of sewer pipes, and can reduce the sewer service life significantly. To facilitate the management and maintenance of sewers, it is essential to obtain reliable prediction of the expected service life of sewers, especially if that is based on limited environmental conditions. Recently, a long-term study was performed to identify the controlling factors of concrete sewer corrosion using well-controlled laboratory-scale corrosion chambers to vary levels of H 2 S concentration, relative humidity, temperature and in-sewer location. Using the results of the long-term study, three different data-driven models, i.e. multiple linear regression (MLR), artificial neural network (ANN), and adaptive neuro fuzzy inference system (ANFIS), as well as the interaction between environmental parameters, were assessed for predicting the corrosion initiation time (t i) and corrosion rate (r). This was performed using the sewer environmental factors as the input under 12 different scenarios after allowing for an initiation corrosion period. ANN and ANFIS models showed better performance than MLR models, with or without considering the interactions between environmental factors. With the limited input data available, it was observed that t i prediction by these models is quite sensitive, however, they are more robust for predicting r as long as the H 2 S concentration is available. Using the H 2 S concentration as a single input, all three data driven models can reasonably predict the sewer service life. Graphical abstract Image 1 Highlights • Data-driven models developed to predict service life of concrete sewers. • Interactions between input parameters affecting predictions were detected. • Model performance was systematically evaluated for 12 scenarios of partial inputs. • Sewer service life can be reasonably predicted by use of H 2 S concentration alone. [ABSTRACT FROM AUTHOR]

Abstract The present work proposes a new solar power station surveillance approach based on the photovoltaic module failures diagnosis using an adaptive neuro-fuzzy inference approach. Indeed, the main aim of this proposed approach is to ensure and increased energy efficiency and improved reliability of the studied solar power station. This approach is used to generate faults indicators, to detect, locate and isolate the faults based on modeling of the main characteristic variables based on an adaptive neuro-fuzzy inference, where the main aim is the prediction of the expected studied system behavior based on the actual collected measurements of the studied system. Where, the investigation field of this work is implanted on an area of 60 ha it contains 120120 solar panels with an efficiency of 15–20% with a total power of 30 MW connected to the electrical network of 30 KV. The obtained results confirm the validity of the proposed approach in improving the reliability and the overall efficiency of the studied power system. It was proved experimentally that after a sandstorm, the normal operating mode thresholds were exceeded and absolute overshoots of 35.5, 5.6 and 1.3 were registered for the output power, the output voltage and the output current respectively. These registrations have permitted to identify the failures and to set up a decision for the cleaning of the photovoltaic module. Indeed, It has been proved in this work that the operation state mode can be maintained based on failure detection and its maintenance which can be achieved in time thanks to the proposed approach. Graphical abstract Photovoltaic system diagnostic based on ANFIS model. Image 1 Highlights • Failures diagnosis in solar power components for efficient sustainable energy. • Monitoring strategy based on failure diagnosis for solar power station is proposed. • The ANFIS approach is used for failure diagnosis in the studied solar power system. • The obtained ANFIS models of the solar system are validated using real data. • Comparison between real and reference behavior in different operating mode. [ABSTRACT FROM AUTHOR]

Accurate estimation of methane viscosity is extremely important for petroleum engineers. Methane viscosity as an important property is used to model multiphase fluid flow in porous media. The viscosity of methane is usually presented as a function of pseudoreduced pressure (Ppr) and pseudoreduced temperature (Tpr) and can be obtained using correlations and charts. Nevertheless, the prediction of methane viscosity at high pressure and high pressure (HPHT) by correlations is associated with some level of uncertainties. Moreover, in the available charts, the methane viscosities are not presented at HPHT conditions. Therefore, having an accurate model that can predict the viscosity of methane at HPHT conditions is beneficial. Therefore, in this study, adaptive neuro-fuzzy inference system (ANFIS) as a powerful intelligent tool is used to predict the viscosity of methane at HPHT using literature experimental data. Two hybrid ANFIS-based models are developed. In the first model, particle swarm optimization (PSO) is employed to find the optimum ANFIS model parameters (ANFIS-PSO), while in the second model, the genetic algorithm (GA) optimization is applied (ANFIS-GA). The results show a better prediction of methane viscosity by the ANFIS models compared to those by models and correlations from the literature. Moreover, ANFIS-GA model shows slightly better prediction than the ANFIS-PSO model. In fact, ANFIS-GA model possesses the lowest average absolute relative deviation, the lowest mean squared error, and the highest correlation coefficient. The findings from the present work demonstrate that the proposed ANFIS-GA model can be easily implemented in any reservoir simulation software, and it provides superior accuracy and performance in reservoir simulators. [ABSTRACT FROM AUTHOR]

Static Poisson's ratio (vstatic) is a key factor in determine the in-situ stresses in the reservoir section. vstatic is used to calculate the minimum horizontal stress which will affect the design of the optimum mud widow and the density of cement slurry while drilling. In addition, it also affects the design of the casing setting depth. vstatic is very important for field development and the incorrect estimation of it may lead to heavy investment decisions. vstatic can be measured in the lab using a real reservoir cores. The laboratory measurements of vstatic will take long time and also will increase the overall cost. The goal of this study is to develop accurate models for predicting vstatic for carbonate reservoirs based on wireline log data using artificial intelligence (AI) techniques. More than 610 core and log data points from carbonate reservoirs were used to train and validate the AI models. The more accurate AI model will be used to generate a new correlation for calculating the vstatic. The developed artificial neural network (ANN) model yielded more accurate results for estimating vstatic based on log data; sonic travel times and bulk density compared to adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) methods. The developed empirical equation for vstatic gave a coefficient of determination (R2) of 0.97 and an average absolute percentage error (AAPE) of 1.13%. The developed technique will help geomechanical engineers to estimate a complete trend of vstatic without the need for coring and laboratory work and hence will reduce the overall cost of the well. [ABSTRACT FROM AUTHOR]

This work develops digital entities of a commercial Fresnel Solar Collector (FSC) installed in an absorption cooling plant. The objective is to create and validate models that describe the FSC dynamics across its whole operation range during the day and the night. Thus, the temperatures range between operation temperature of 180 ° C and almost ambient temperature due to overnight heat losses. In the same sense, the flow range between zero to 13 m 3 / h. The idea is that the digital twin will aid start-up and shut-down optimization and control design reliability. The paper employs two modeling approaches, then evaluates their twinning/adaptation time and performance validation. One model uses phenomenological modeling through Partial Differential Equations (PDE) and parameters identification, and another uses a data-driven technique with Adaptive Neuro-Fuzzy Inference Systems (ANFIS). The available measurement data sets comprise 25 days of operation with a sampling time of 20 s which, after outlier removal, filtering and treatment, resulted in 108416 samples. The validation considers six separate operating days. Results show that both models can twinning/adapt considering measured data. The models present pretty good results and are suitable for control and optimization. Besides, this is the first paper considering the FSC mirror defocus action on dynamic modeling and validation. • This work validates neuro-fuzzy (NF) and differential (PDE) models with massive data. • The models generally represent the process day and night. • The models are fairly accurate and precise—worst-case MAPE of 2.49%. • The NF model has ten times faster simulation time than the PDE model. • The validated dynamic models are the first accounting with mirror's focus action. [ABSTRACT FROM AUTHOR]

The distillation column is one of the most important unit operations used in the chemical engineering. The continuous distillation process is largely used in many applications such as petrochemical production, natural gas processing, and petroleum refineries, and many others. Corrective maintenance of the chemical reactors represents a consequential problem because it is very costly and it disrupts production for long periods of time. In addition, most of the time, this may lead to harmful effects and disastrous results. The most common solution has been to rely on preventive maintenance. Unfortunately, this has been both expensive and inadequate. Therefore, the optimal solution is to resort to predictive maintenance that involves the design of a pre-crash control system and a higher ex-ante understanding of the future path of the reactor. This research paper aims to propose the Adaptive Neuro Fuzzy Inference System (ANFIS) as a superior technique that can forecast the future path of the distillation column system. In addition, this paper will propose Parzen windows distribution as a new membership function in order to improve ANFIS performance either by reducing consumption time and making processing closer to real-time application, or by minimizing the root means square error (RMSE) between real and predictive data. This methodology was tested on real experimental data obtained from a distillation column with the aim of predicting failures that may possibly occur during the automated continuous distillation process. A comparative study was necessary in order to properly select the superior membership function that can be used for the ANFIS algorithm when ANFIS is applied to the distillation column data. Results demonstrated the importance of the proposed technique since it proved to be highly successful in terms of reducing time consumed. Additionally, Parzen windows had the smallest RMSE for many signals in both normal and degraded modes. [ABSTRACT FROM AUTHOR]

The size of a software project is a key measure of predicting software effort at the requirements and analysis phase. Use case points (UCP) is among software size metrics that achieved good reputation because of the increasing popularity of use case driven development methodologies in software industry. Nevertheless, there is no consistent method that can effectively translate the UCP into its corresponding effort. Previous estimation models were built using a very limited number of projects, and they were not well examined. The soft computing techniques were rarely applied for such problem and their performances have not been well investigated using a systematic procedure. This study looks into the accuracy and stability of some soft computing methods for the problem of effort estimation based on UCP. Four neural network methods, adaptive neuro fuzzy inference system and support vector regression have been used in this comparative study. The results suggest that most used soft computing techniques can work well with good accuracy for such problem. Among them, the general regression neural network is the superior one with stable ranking across different accuracy measures. Also, it has been found that using adjustment variables with basic UCP variables, solely or together, have positive impact on the accuracy and stability. [ABSTRACT FROM AUTHOR]

The aim of this study is to examine the potential of adaptive neuro fuzzy inference system (ANFIS) to estimate biochemical oxygen demand (BOD). To illustrate the applicability of ANFIS method, the upstream catchment of Feitsui Reservoir in Taiwan is chosen as the case study area. The appropriate input variables used to develop the ANFIS models are determined based on the t-test. The results obtained by ANFIS are compared with those by multiple linear regression (MLR) and artificial neural networks (ANNs). Simulated results show that the identified ANFIS model is superior to the traditional MLR and nonlinear ANNs models in terms of the performance evaluated by the Pearson coefficient of correlation, the root mean square error, the mean absolute percentage and the mean absolute error. These results indicate that ANFIS models are more suitable than ANNs or MLR models to predict the nonlinear relationship within the variables caused by the complexity of aquatic systems and to produce the best fit of the measured BOD concentrations. ANFIS can be seen as a powerful predictive alternative to traditional water quality modeling techniques and extended to other areas to improve the understanding of river pollution trends. [ABSTRACT FROM AUTHOR]

Adaptive neuro fuzzy inference system (ANFIS) and genetic algorithm–artificial neural network (GA-ANN) models were used to predict the effect of methyl jasmonate (at three levels 0, 0.01, and 0.1 mM) and storage time (0, 14, 28, 42, 56, 70, and 84 days) on quality parameters and physiological changes of pomegranate fruits during storage. Methyl jasmonate reduced chilling injury and improved quality characteristics of pomegranates during postharvest storage. The GA-ANN and ANFIS were fed with two inputs of methyl jasmonate and storage time. The results showed that GA-ANN predictions agreed with experimental data and the GA-ANN with 14 neurons in one hidden layer can predict physiological changes and quality parameters of pomegranate (weight loss, pH, chilling injury index, ion leakage, ethylene, respiration, polyphenols, anthocyanins, and total antioxidant activity) with correlation coefficients equal to 0.87. The ANFIS model was trained by a hybrid method and agreement between experimental data and ANFIS predictions was significant (r= 0.90). [ABSTRACT FROM AUTHOR]

Ammonia (NH 3 ) is considered one of the significant pollutions contributor to indoor air quality and odor gas emission from swine house because of the negative impact on the health of pigs, the workers and local environment. Prediction models could provide a reasonable way for pig industries and environment regulatory to determine environment control strategies and give an effective method to evaluate the air quality. The adaptive neuro fuzzy inference system (ANFIS) simulates human’s vague thinking manner to solve the ambiguity and nonlinear problems which are difficult to be processed by conventional mathematics. Five kinds of membership functions were used to build a well fitted ANFIS prediction model. It was shown that the prediction model with “Gbell” membership function had the best capabilities among those five kinds of membership functions, and it had the best performances compared with backpropagation (BP) neuro network model and multiple linear regression model (MLRM) both in wintertime and summertime, the smallest value of mean square error (MSE), mean absolute percentage error (MAPE) and standard deviation (SD) are 0.002 and 0.0047, 31.1599 and 23.6816, 0.0564 and 0.0802, respectively, and the largest coefficients of determination (R 2 ) are 0.6351 and 0.6483, repectively. The ANFIS prediction model could be served as a beneficial strategy for the environment control system that has input parameters with highly fluctuating, complexity, and non-linear relationship. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for Loss of Excitation (LOE) faults detection in hydro-generators using adaptive neuro fuzzy inference system. The proposed scheme was trained by data from simulation of a 345 kV system under various faults conditions and tested for different loading conditions. Details of the design process and the results of performance using the proposed technique are discussed in the paper. Three techniques are used in this article based on the type of inputs to the proposed ANFIS unit, the generator terminal impedance measurements (RandX), the generator RMS Line to Line voltage and Phase current (VtrmsandIa) and the positive sequence components of the generator voltage magnitude, phase current magnitude, and angle (│V+ve│, │I+ve│, and ∟I+ve). The obtained results from these schemes are compared with each other and are compared with other techniques. The results show that the proposed technique is effective in detecting the LOE faults and the obtained results are very encouraging. [ABSTRACT FROM PUBLISHER]

One of the most important challenges in microgrid operation is the unintentional islanding occurrence. Unintentional islanding can cause serious safety hazards and technical issues. Islanding detection methods can be classified into active and passive methods. The main disadvantages of the passive methods are large non‐detection zone as well as determination of suitable threshold value to avoid unwanted distributed generations tripping in normal network events. In order to overcome these drawbacks, this study proposes a novel, fast, and reliable method to identify islanding conditions. The proposed method calculates different transient states in the rate of change of frequency signal in two consecutive cycles. Various features of the differential signal are extracted. The extracted feature vectors associated with different operation conditions such as islanding and non‐islanding events are used to train artificial neural networks (ANNs). The performances of different structures of ANNs and also other machine learning methods such as support vector machine and adaptive neuro fuzzy inference system are evaluated for islanding detection purposes. The simulation results indicate that the proposed method provides more accurate and faster responses compared with other conventional islanding detection methods. [ABSTRACT FROM AUTHOR]

This paper proposes the design of auxiliary-coordinated controller for static VAR compensator (SVC) and thyristor-controlled series capacitor (TCSC) devices by adaptive fuzzy optimized technique for oscillation damping in multimachine power systems. The performance of the coordinated control of SVC and TCSC devices based on feedforward adaptive neuro fuzzy inference system (F-ANFIS) is compared with that of the adaptive neuro fuzzy inference system (ANFIS) structure based on recurrent adaptive neuro fuzzy inference system (R-ANFIS) network architecture. The objective of the coordinated controller design is to tune the parameters of SVC and TCSC fuzzy lead lag compensator simultaneously to minimize the deviation of rotor angle and rotor speed of the generators. The performance of the system is enhanced by optimally tuning the membership functions of fuzzy lead lag controller parameter of the flexible AC transmission system (FACTS) by R-ANFIS controller. The training data for F-ANFIS and R-ANFIS are generated by conventional linear control technique under various operating conditions. The offline trained controller tunes the parameter of lead lag controller in online. The oscillation damping ability of the system is analyzed for three-machine test system by calculating the standard deviation and cost function. The superior performance of R-ANFIS controller is compared with various particle swarm optimization-based feedforward ANFIS controllers available in literature. [ABSTRACT FROM AUTHOR]

In this paper, a channel estimator based on an adaptive neuro fuzzy inference system (ANFIS) is proposed for the purpose of estimating channel frequency responses in orthogonal frequency division multiplexing-interleave division multiple access (OFDM-IDMA) systems. To see the performance of our proposed channel estimation method, five different techniques including well-known pilot-based estimation algorithms such as least squares (LS) and minimum mean square error (MMSE) with other heuristic methods like multilayered perceptron (MLP) trained by a backpropagation (BP) algorithm (MLP-BP), MLP trained by the Levenberg{Marquardt (LM) algorithm (MLP-LM), and radial basis function neural network (RBFNN) are compared with our proposed method by computer simulations. The comparisons are made with the aid of bit error rate and mean square error graphs. According to the simulation results, our proposed channel estimator based on ANFIS shows better performance than both the LS algorithm and the other considered heuristic methods like MLP-BP, MLP-LM, and RBFNN, whereas the MMSE algorithm still shows the best performance as expected because of exploiting channel statistics and noise information, which makes it very complex to be used in any system. As well as being less complex compared to the MMSE algorithm, the estimator based on ANFIS does not need pilot tones for channel estimation. These properties bring our proposed method to an advantageous position among the other estimation techniques. [ABSTRACT FROM AUTHOR]

Nuclear power plant (NPP) such as Prototype Fast Breeder Reactor (PFBR), is a paradigm of complex engineering which is safety critical in nature. It sends copious plant signals to the main control room. An amalgamation of plenty of such plant signals escorts an operator to make appropriate decision during catastrophic circumstances. This decision must be quick and unambiguous in order to overcome any adverse situation. The concept of dimensionality reduction aids the purpose as it reduces the number of inputs to any system or model or classifier which ultimately makes the decision process faster. The fundamental aspect which should be taken paramount care is that there should not be any loss of information due to dimensionality reduction, as in a NPP, safety is the foremost goal. One of the most prevalently used dimensionality reduction algorithms is principal component analysis (PCA). This is achieved by dumping the principal components which has less variability. In this paper, the feasibility of dimensionality reduction is studied for classification of some of the events in PFBR. Two cases are considered in this paper out of which in first one, the event is divided into two sub events such as primary and secondary part of the event based on the importance of classification. The event data is fed to two separate classifiers which classify both the parts of the event separately. Finally, the event is classified as the concatenation of the outputs of both the classifiers. Unlike the first case, where two classifiers identify the event, here, in second case, a single classifier does the event classification. The classifier used is the adaptive neuro fuzzy inference system (ANFIS). This classifier has the advantage of both neural networks and fuzzy system where the neural network concept is used to tune the fuzzy membership function. PCA is used for dimensionality reduction and scree test for factor analysis. The performance of the PCA-ANFIS classifier is measured by calculating the area under the receiver operating characteristics curve (AUC) which is one of the most popularly used performance measures for any classifier. A comparative study is done on the AUC of all the PCA-k-ANFIS classifiers for both the cases mentioned above. Here, k represents the number of principal components considered as input data to the ANFIS classifier. A study on the significance of PCA along with ANFIS is also carried out by comparing it with an ANFIS classifier without using PCA. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach for cost and risk assessment in the multi-objective selection of routes for the transport of hazardous materials (hazmat) on a network of city roads. The model is based on the application of an Adaptive Neuro Fuzzy Inference System (ANFIS). The values of the cost and risk criteria are, using an adaptive neuro-fuzzy network trained with an Artificial Bee Colony (ABC) algorithm, integrated into a single CR value by means of which the worthiness of each branch in the network is expressed, and after which the selection of the route is made using Dijkstra's algorithm. The ANFIS adequately treats a number of uncertainties and ambiguities in the input data and enables the inclusion of the knowledge of experts and the preferences of the decision makers. The procedure is also applicable in cases in which the decision maker does not have high quality data available. The proposed model is tested in a real urban route planning problem, in a case study of the distribution of oil and oil derivatives in Belgrade, Serbia. [ABSTRACT FROM AUTHOR]

In this paper, STATCOM performance for voltage–reactive power control is investigated by comparing different tuning methods, used to evaluate gain parameters of STATCOM controller in presence of high probabilistic uncertainty in input wind power and reactive power load demand. To control voltage transient response in least time, reactive power demand is managed by STATCOM. The conventional methods for tuning gain parameters of STATCOM controller do not satisfactorily operate in case of random disturbances and therefore, advanced controllers such as Genetic Algorithm (GA), Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) are required. The main contribution of the paper is: (i) Investigation of STATCOM performance in presence of high probabilistic uncertainty with step changes in input wind power and reactive power load demand, (ii) system studies during dynamic conditions with composite load model in lieu of static load model in the system, (iii) comparison of voltage control and STATCOM reactive power using various tuning methods. Results comparison through all tuning methods show that advanced tuning methods are able to preserve optimal performances over wide range of disturbances using Integral of Square of Errors (ISE) criterion. [ABSTRACT FROM AUTHOR]

Supplier evaluation and selection constitutes a central issue in supply chain management (SCM). However, the data on which to base the corresponding choices in real life problems are often imprecise or vague, which has led to the introduction of fuzzy approaches. Predictive intelligent-based techniques, such as Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS), have been recently applied in different research fields to model fuzzy multi-criteria decision processes where the understanding and learning of the relationships between the input and output data are the key to select suitable solutions. In this paper, a hybrid ANFIS-ANN model is proposed to assist managers in their supplier evaluation process. After aggregating the data set through the Analytical Hierarchy Process (AHP), the most influential criteria on the suppliers’ performance are determined by ANFIS. Then, Multi-Layer Perceptron (MLP) is used to predict and rank the suppliers’ performance based on the most effective criteria. A case study is presented to illustrate the main steps of the model and show its accuracy in prediction. A battery of parametric tests and sensitivity analyses has been implemented to evaluate the overall performance of several models based on different effective criteria combinations. [ABSTRACT FROM AUTHOR]

The adaptive neuro fuzzy inference system (ANFIS) has been proposed to model the time series of water quality data in this study. The biochemical oxygen demand data collected at the upstream catchment of Feitsui Reservoir in Taiwan for more than 20 years are selected as the target water quality variable. The classical statistical technique of the Box-Jenkins method is applied for the selection of appropriate input variables and data pre-processing of using differencing is implemented during the model development. The time series data obtained by ANFIS models are compared to those obtained by autoregressive integrated moving average (ARIMA) and artificial neural networks (ANNs). The results show that the ANFIS model identified at each sampling station is superior to the respective ARIMA and ANN models. The R values at all sampling stations of the training and testing datasets are 0.83-0.98 and 0.81-0.89, respectively, except at Huang-ju-pi-liao station. ANFIS models can provide accurate predictions for complex hydrological processes, and can be extended to other areas to improve the understanding of river pollution trends. The procedure of input selection and the pre-processing of input data proposed in this study can stimulate the usage of ANFIS in other related studies. [ABSTRACT FROM AUTHOR]

Forecasting stock price is a hot issue for stock investors, dealers and brokers. However, it is difficult to find out the best time point to buy or sell stock, since many variables will affect the stock market, and stock dataset is time series data. Therefore many time series models have been proposed for forecasting stock price; furthermore the previous time series methods still have some problems. Hence, this paper proposes a novel ANFIS (Adaptive Neuro Fuzzy Inference System) time series model based on integrated nonlinear feature selection (INFS) method for stock forecasting. Firstly, this study proposed an integrated nonlinear feature selection method to select the important technical indicators objectively. Secondly, it used ANFIS to build time series model and test forecast performance, then utilized adaptive expectation model to strengthen the forecasting performance. In order to evaluate the performance of proposed model, the TAIEX and HSI stock market transaction data from 1998 to 2006 are collected as experimental dataset and compared with other models. The results show that the proposed method outperforms the listing models in accuracy, profit evaluation and statistical test. [ABSTRACT FROM AUTHOR]

This study applies probabilistic neural network (PNN) and adaptive neuro fuzzy inference system (ANFIS) to develop bus service quality (SQ) prediction model based on the preferences stated by users (on a scale of 1 to 5). A questionnaire survey is conducted and a data set from the survey is prepared to develop the SQ prediction model using PNN and ANFIS. Results show that ANFIS produced better prediction than PNN. The research is further extended to include ranking of the SQ attributes according to their impact on the overall result from the developed model. Attributes such as punctuality and reliability, seat availability, and service frequency were found to be the top three attributes that mostly affect the decision making process of the users. This study can aid service providers in improving the most important attributes of bus service to develop the quality of service, thereby increasing transit ridership. [ABSTRACT FROM AUTHOR]

Modeling of wind turbine power curve is greatly important in performance monitoring of the turbine and also in forecasting the wind power generation. In this paper, an accurate parametric model called modified hyperbolic tangent (MHTan) is proposed to characterize power curve of the wind turbine. The paper also presents the development of both parametric and nonparametric models of wind turbine power curve. In addition, least square error (LSE) and maximum likelihood estimation (MLE) are employed to estimate vector parameter of the proposed model. Here, three evolutionary algorithms, namely, particle swarm optimization, Cuckoo search, and backtracking search algorithm aid LSE and MLE. The performance of all presented methods is evaluated by a real data collected from a wind farm in Iran as well as three statistically generated data sets. The results demonstrate the efficiency of the proposed model compared to some other existing parametric and nonparametric models. [ABSTRACT FROM AUTHOR]

The present paper proposes a novel method to eliminate harmonics in a solar powered CHMLI (cascaded H-bridge multilevel inverter). The problem uses Newton Raphson and PSO (Particle Swarm Optimization) based SHE (Selective Harmonic Elimination) techniques for solving the non-linear transcendental equations and to obtain the optimal switching angles. The optimized switching angles obtained offline are utilized to minimize the THD (Total Harmonic Distortion). The major concern in the harmonic-elimination problem in the non-equal dc sources system is the solution of the nonlinear transcendental equations. However, the proposed method can solve this problem in a simpler manner, by just converting non-equal dc sources into equal dc source using the ANFIS (Adaptive Neuro Fuzzy Inference System)/Constant Voltage Maximum Power Point Tracker algorithm for the Photo-voltaic system. The performance are evaluated using simulation results for PV fed 11-level cascade H-bridge inverter with induction motor drive. The simulation results are validated using an experimental model of CHMLI with Xilinx Spartan ® -3A DSP FPGA based controller. [ABSTRACT FROM AUTHOR]

Introduction: Forecasting of the wastewater quality parameters has great importance in modern wastewater treatment methods. One of the main problems in predicting the efficacy of a wastewater treatment is the complexity of physicochemical properties of raw sewage and data differences for different reasons. Modeling of wastewater treatment using Adaptive Neural Fuzzy Inference System (ANFIS)- can help to improve wastewater quality control process. The aim of the study was to modeling of treatment of dairy wastewaters by electrocoagulation process using ANFIS. Methods: In this study, ANFIS was used to estimate the chemical coagulation of dairy wastewater treatment. The input parameters to the ANFIS model were time, voltage, total suspended solids and boichemical oxygen demand and the output was chemical oxygen demand removal efficiency. Also the membership functions, the number of membership functions and number of learning cycles (Epochs) were used for optimization of different models by trial and error. Results: The best model was assessed by bell-shaped membership functions with number of membership functions as 3 3 3 3 3 and 300 epochs of training with lowest mean square error(MES) and the best coefficient of determination (R2). The coefficient of determination and MSE of the best ANFIS model were 0.9912 and 0.012, respectively. Conclusion: Analysis of the model revealed that the ANFIS is a powerful tool to predict the dairy wastewater treatment using electrical coagulation. [ABSTRACT FROM AUTHOR]

Complex coacervation is a common technique that enables the encapsulation of nutraceuticals/bioactive compounds and the modification of biopolymeric properties. Here, the complex coacervation occurred between cress seed mucilage (CSM) and β-Lactoglobulin (Blg) and the results were systematically evaluated under the influence of polymer concentrations (CSM & Blg), pH and Blg: CSM mixing ratio. In this regard, thirty-six experiments were performed, including particle size and zeta potential measurements to find the optimum reaction conditions. Then, Response Surface Methodology (RSM) and Adaptive Neuro Fuzzy Inference System (ANFIS) were applied to predict model-effects and to reach optimization in experimental variables on the size and zeta potential of CSM-Blg complexes. The correlation coefficient of RSM (size: 0.984, zeta potential: 0.975) and ANFIS models (size: 0.998, zeta potential: 0.997) were acceptable in the case of both responses. They also indicated a higher correlation of the predicted values with the experimental data by ANFIS. According to RSM results, CSM concentration and pH were the most important variables in determining both size and zeta potential of complexes. In addition, the best conditions (CSM ​= ​0.1 %w/v, Blg ​= ​0.066 %w/v, pH ​= ​5.0 and mixing ratio ​= ​1.25) were calculated by ANFIS for the formation of the smallest and most stable CSM-Blg complexes. The complexity and nonlinearity of the relationships between the input and output variables further revealed the ability of intelligent models such as ANFIS in predicting and controlling the complex properties. This ability of the model was consistent with experimental inferences. • Detailed optimization of synthesizing the cress seed mucilage (CSM) and β-Lactoglobulin (Blg) complexes via RSM and ANFIS models. • Statistical analysis of the models showed ANFIS was superior to RSM for the purpose of this research. • The complexity relation between the variables in 3D plots broke the simplicity of the complex coacervation method. • The relationships between variables in 3D plots showed that the ANFIS was in agreement with the experimental inferences. [ABSTRACT FROM AUTHOR]

• A SVNN-ANFIS approach for stability evaluation of open-pit mine slopes is proposed. • The approach integrates SVNN into ANFIS as a novel AI framework (SVNN-ANFIS). • The prediction accuracy of the new approach for slope stability reaches 97.6%. The stability of the open-pit mine slope has an important impact on the safe operation and benefits of the mining company. This paper proposes a single-valued neutrosophic number-based adaptive neuro fuzzy inference system (SVNN-ANFIS) approach to evaluate the open-pit mine slope stability. In the method, it utilizes the single-valued neutrosophic number concept for expressing the truth, indeterminate, and falsity information of influencing factors/indices (slope height, slope angle, potential slip plane angle, cohesion, internal friction angle and rock density) of the open-pit mine slope stability by truth, indeterminate, and falsity membership functions. Then, three ANFISs are used to obtain the corresponding relations between the influencing factors and the slope stability in truth, indeterminate, and falsity situations. Finally, the truth, indeterminate, and falsity output of the three ANFISs, i.e., a SVNN output, whose score value can be used to evaluate the slope stability through a score function of SVNN. The application results of the proposed approach have reflected that the training accuracy reaches 99.20% and the testing accuracy reaches 97.62%. Therefore, the established SVNN-ANFIS method provides a new effective way for evaluating open-pit mine slope stability in the uncertain environment. [ABSTRACT FROM AUTHOR]

A generic oil-hydraulic based power take-off concept applicable to different Wave Energy Converters is presented. This power take-off is developed to have well-adapted characteristics to larger power and low frequency waves and to minimize spring, oscillatory and transient effects of the oil caused by the operation of valves which lead to the damage of pipelines, pumps and accessories. The proposed concept adopts technology already tested in other fields and uses hydraulic transformers and speed control of their secondary drives. Moreover this paper contributes to a better understanding of the implications of using hydraulic transformers in the power take-off system. According to the simulations, approximately 76% of the power take-off hydraulic efficiency might be achieved in the two and most energetic of the three simulated sea state conditions and the ones between these two. The transformer is not used in the less energetic sea state condition just because the use of reactive control is less effective than using damping control. Even in this case the PTO efficiency is 71%. So, using a hydraulic transformer in the power take-off system can be beneficial. [ABSTRACT FROM AUTHOR]

Valence–Arousal is regarded as a reflection of KANSEI adjectives, which is the core concept in the theory of emotional dimensions for brain recognition. This paper presents a novel method for determining the characteristics of Valence-Arousal-based timing signals using Power Spectrum Density (PSD) of fMRI images, and Gaussian filtering, segmenting, and Gaussian-shaped Fast Fourier Transform (FFT) will be applied for reprocessing fMRI images; the timing characteristics of the fMRI image signals were extracted under short-term emotional picture stimuli (within 6 s). To reduce the computational complexity, a cubic curve fitting method was used to smooth the Valence–Arousal timing curve, and the coefficients of the fitted curve, the mean, and the standard deviation were derived from the Gaussian-shaped Affective Norm English Words (ANEW) system, subsequently, these parameters were selected to create a 4-INPUT 2-OUTPUT Takagi–Sugeno (T–S) type Adaptive Neuro Fuzzy Inference System (ANFIS). In the experimental study, an fMRI data-set was acquired for KANSEI-“kindness” picture stimuli and the FIS prediction was 0.05 less than the Root Mean Square Error (RMSE) after 24/18 iteration epochs for Valence/Arousal. These experiments showed that the proposed method effectively simplified high complexity when calculating fMRI images. The cubic curve fitting method extracted the characteristics of the Valence–Arousal time series-based curves effectively and established the KANSEI adjective content more accurately by comparing with the ANEW system of Valence–Arousal values. The proposed curve generation methods for the Valence–Arousal response of KANSEI adjectives will be a potential application for attention-oriented product design fields. [ABSTRACT FROM AUTHOR]

The emission of hydrocarbon plays a key role in the oil and gas production industries and can pose a danger. In the current cooperation, two intelligent simple tools, namely, support vector machine (SVM) and adaptive network based fuzzy inference system (ANFIS), have been developed to predict the amount of filling loss in storage tanks at vapor pressures ranging between 0 and 101 KPa and working pressures ranging between 101.325 and 251.325 KPa. Based on statistical analysis, estimations by the SVM approach show better accuracy than the ANFIS method. The proposed models are easy to apply and would be of great assistance to engineers, particularly those dealing with the design and applications of storage tanks. The efforts in this study will cover the manner for making precise estimations of the filling losses in storage tanks, which can help researchers and engineers control the operational conditions. [ABSTRACT FROM AUTHOR]

This article adopts Multivariate Adaptive Regression Spline (MARS) for prediction of Angle of Shearing Resistance(ϕ) of soil. MARS is an adaptive, non-parametric regression approach. Percentages of fine-grained (FG), coarse-grained (CG), liquid limit (LL), and bulk density (BD) have been used as input variables of MARS. The developed MARS gives an equation for prediction ofϕof soil. The results of MARS have been compared with Genetic Expression Programming (GEP), Artificial Neural Network (ANN), and Adaptive Neuro Fuzzy Inference System (ANFIS) models. These results demonstrate that the developed MARS can be used as a robust model for determination ofϕof soil. [ABSTRACT FROM AUTHOR]

The authors of this article deals with the implementation of a combination of techniques of the fuzzy system and artificial intelligence in the application area of non-linear noise and interference suppression. This structure used is called an Adaptive Neuro Fuzzy Inference System (ANFIS). This system finds practical use mainly in audio telephone (mobile) communication in a noisy environment (transport, production halls, sports matches, etc). Experimental methods based on the two-input adaptive noise cancellation concept was clearly outlined. Within the experiments carried out, the authors created, based on the ANFIS structure, a comprehensive system for adaptive suppression of unwanted background interference that occurs in audio communication and degrades the audio signal. The system designed has been tested on real voice signals. This article presents the investigation and comparison amongst three distinct approaches to noise cancellation in speech; they are LMS (least mean squares) and RLS (recursive least squares) adaptive filtering and ANFIS. A careful review of literatures indicated the importance of non-linear adaptive algorithms over linear ones in noise cancellation. It was concluded that the ANFIS approach had the overall best performance as it efficiently cancelled noise even in highly noise-degraded speech. Results were drawn from the successful experimentation, subjective-based tests were used to analyse their comparative performance while objective tests were used to validate them. Implementation of algorithms was experimentally carried out in Matlab to justify the claims and determine their relative performances. [ABSTRACT FROM AUTHOR]

This paper presents an intelligent Proportional Integral Derivative (PID) controller for Automatic Voltage Regulator (AVR) system using Adaptive Neuro Fuzzy Inference System (ANFIS). In the proposed method, the PID controller parameters are tuned off line by using combination of Signal to Noise Ratio (SNR) and Particle Swarm Optimization (PSO) algorithm to minimize the cost function over a wide range of operating condition. The optimal values of PID controller parameters obtained from SNR-PSO algorithm for each considered operating condition are used to train ANFIS. Therefore, the proposed techniques could online tune the PID controller parameters at any other operating condition to improve the transient response of the system. In order to evaluate the performance of the SNR-PSO PID controller, the results are compared with the Genetic Algorithm (GA). Also, the performance of proposed intelligent PID controller is compared with the robust SNR-PSO PID controller with fixed parameters. The comparison shows the SNR-PSO based ANFIS controller is more efficiency than robust PID controller. [ABSTRACT FROM AUTHOR]

In energy sector, accurate prediction of long term gas consumption is very important for decision-making and policy process. In addition, conventional approaches may not provide precise results. In this paper, an integrated forecasting algorithm based on Adaptive Neuro Fuzzy Inference System and Computer Simulation (ANFIS-CS) for long term gas consumption has been proposed. Standard input variables include different economic, environmental and IT/IS (number of internet users divided by population in each year) indicators, and the output variable is gas consumption. The concepts of post-processing and pre-processing are considered in the proposed method. At first the best distribution function is identified for each year and then CS is used to create random variables for each year to predict the effects of probabilistic distribution on annual gas consumption. Finally, data is fed into ANFIS model to find the network with the lowest mean absolute percentage error (MAPE). To show the quantitative benefits of the ANFIS-CS, 12 different structures of a well-known class of adaptive neural networks (ANNs), namely Multi-Layer Perceptron (MLP) as well as 10 different types of regression models are developed and the MAPE values of ANN-MLP models and regression models are compared with the MAPE of proposed model. The results of this comparison show the applicability and superiority of the proposed method. This is the first study that presents an integrated intelligent forecasting approach for accurate gas consumption considering the economic, environmental and IT/IS indicators. [ABSTRACT FROM AUTHOR]