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In the presence of increasing demands for safety and efficiency of material handling systems, the development of advanced supervisory control, monitoring, data acquisition and diagnostic systems is involved, especially for large industrial cranes. The important part of such systems is the continuous monitoring of a crane load. The crane load monitoring system proposed in the paper is based on a fuzzy model that estimates a payload mass transferred by a crane based on measuring the crane girder deflection and trolley position. The model was identified using the fuzzy subtractive clustering and least mean square with the data collected during experiments carried out on the laboratory scaled overhead crane. [ABSTRACT FROM AUTHOR]

The precision agriculture and soil tillage are tasks which can be achieved by Automated Tractors (AT) through the integration of several servomechanisms. In order to reach a high autonomy under various work conditions of the AT, control laws' design and tuning are paramount. The aim of this work is to develop an angle position controller for steering systems applied to an AT. We introduce an automatic electronic steering system by using a fuzzy model, obtained through an Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm training. A proportional derivative (PD) controller is also tuned through a Takagi-Sugeno fuzzy model (T-S). Furthermore, several closed-loop stability tests were carried out, in numerical simulation and real-time implementation. The feasibility of our methodology is illustrated through the tracking of several angles' profiles in a real test scenario. For the tracking of a trajectory or several set-points, we have obtained mean errors about 0.6545 and 0.8651 degrees, respectively. Some mechatronic integrations to convert a conventional tractor into a basic Autonomous Agriculture Off-road Tractor (AAOT) are also shown. [ABSTRACT FROM AUTHOR]

The subcontractor selection decision is inherently a multicriterion problem. It is a decision of strategic importance for companies. The nature of this decision is usually complex and unstructured. Management science techniques might be helpful tools for solving these kinds of decision-making problems. In this research, the fuzzy logic method and the analytic hierarchy process were applied for the selection of suitable subcontractors in an apparel supply chain. In general, many factors, such as quality level, price offer, and delivery delay, were considered to determine the most suitable and reliable subcontractors that fit the company's strategy. This survey is carried out using the database of an apparel company manufacturing denim products. [ABSTRACT FROM AUTHOR]

Internal combustion engines are the inevitable prime movers in the contemporary engineering era. The suitability of proper bio-fuel and their blends plays a vital role in engine behaviour. This study aims to select smart fuel opus depending on Aegle marmelos (AM) fuel properties with nano additive blends for diesel engines by using intelligent hybrid decision-making tools. Physicochemical properties of CuO and novel graphene nano sheets added bio-oil combinations were studied. The assessment of an appropriate blend depends on the analysis of fuel properties. The Fuzzy Analytical Hierarchy Process (FAHP) integrated with Grey relational analysis (GRA) was employed for optimum fuel blend selection. The FAHP model was used to identify the criteria weights, whereas GRA was hired to rank alternative fuel blends. Pairwise analysis and ranking of the alternatives were compared to get the optimum fuel blend through FAHP and GRA amalgamation. The addition of nanoparticles enhanced engine performance and reduced emission. The obtained ascending order of preference of the bio-oil blends from FAHP and GRA analysis is AC15G15>AG30>AC30>A10>A20. From FAHP, GRA, and engine test results, it is observed that AC15G15 opus is the most suitable fuel blend for diesel engines. Lower fuel consumption (0.37 kg/kW hr) and emissions (CO level of 0.21%, which is 0.34% for diesel, HC value of 134 ppm, which is 184 ppm for diesel) of AC15G15 aids in contributing towards a green and clean environment. [ABSTRACT FROM AUTHOR]

The presence of formations containing gypsum layers could enhance the formation of gypsum karst forms. The thickness of the gypsum layers and the quality of the mentioned beds influence the formation of such landforms. The Maroon plain is located in the folded zone of the Zagros Mountains in Kohgiluyeh and Boyer-Ahmad Province. The existence of the Gachsaran Formation and the occurrence of the karst phenomena (among which sinkholes are the most significant) in this plain have a very destructive impact on the roads, facilities, farmlands, residential units, and the reservoir of the Maroon Dam located in the plain. However, this phenomenon is not well-known and well-studied. In the present study, the karst evolution zoning map was prepared using the fuzzy model. To produce this map, ten environmental factors including the slope, slope direction, geology, distance from the Knick line, distance from the flow line, distance from the river, elevation, land use, climate, and the NDVI Index were used. The results of this study demonstrated that 55.2% of the study area was situated in the zero-risk zone because it was situated out of the Gachsaran Formation realm and lacked the gypsum layer. Also, 11.2% of the area was in the high-risk zone, 18.5% in the medium-risk zone, and 15.1% in the low-risk zone. Field observations and studying the zoning map indicated that the main factors influencing the karst evolution of this area were slope and streamlines which were superimposed on the gypsum layers. [ABSTRACT FROM AUTHOR]

The H∞ stability analysis and interval type‐2 Takagi‐Sugeno (T‐S) fuzzy control is studied for a class of interval type‐2 T‐S fuzzy systems. Firstly, the interval type‐2 T‐S fuzzy model is employed to approximate the system plant. Secondly, the delay‐dependent interval type‐2 T‐S fuzzy dynamic output feedback controller is designed. Thirdly, two classes of stability conditions in terms of linear matrix inequalities (LMIs) are derived, and the closed‐loop system is asymptotically stable and prescribed H∞ performance is achieved. The delay‐dependent interval type‐2 T‐S fuzzy dynamic output feedback controller is designed to relax the control design conditions. The descriptor redundancy method is employed for avoiding the crossing vectors between the system gain matrices and controller gain matrices. The delay‐dependent Lyapunov functional with descriptor redundancy matrix is introduced, and the upper and lower membership functions information is considered. Some slack matrices are introduced via the upper and lower membership functions information to solve the obtained LMIs. Finally, simulation examples are performed to show the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

In the present paper, a fuzzy model of assessment is proposed according the framework of fuzzy mathematical theory to estimate the complexity of rock fractures and geological structures. We established the fuzzy evaluation model and decided a trapezoidal membership function using the fuzzy mathematics. Dataset acquisition is integrated with an advanced technique of coupling multiscale fracture data obtained from both high-resolution satellite imagery and field data. Five parameters were selected, including qualitative and quantitative data from different scales, namely fractal dimension, fracture length, fracture intensity, the intersection angle between fractures and beddings and the fracture density. The weight of each factor is obtained with the principle that the relative value of the complexity index is larger and the weight is significant. This model was applied to the complexity assessment of the southwestern Tunisia. The rank of complexity of each sub-area in the study region is from strong to weak. The result shows that Jebal Orbata and Jebal Chamsi have the strongest complexity due to their position beside the 140 dextral strike-slip Gafsa fault. The east and central zone of the region of investigation has the weakest complexity. [ABSTRACT FROM AUTHOR]

Prediction of structural damage prior to earthquake occurrence provides an early warning for stakeholders of building such as owners and urban managers and can lead to necessary decisions for retrofitting of structures before a disaster occurs, legislating urban provisions of execution of building particularly in earthquake prone areas and also management of critical situations and managing of relief and rescue. For proper prediction, an effective model should be produced according to field data that can predict damage degree of local buildings. In this paper in accordance with field data and Fuzzy logic, damage degree of building is evaluated. Effective parameters of this model as an input data of model consist of height and age of the building, shear wave velocity of soil, plan equivalent moment of inertia, fault distance, earthquake acceleration, the number of residents, the width of the street for 527 buildings in the city. The output parameter of the model, which was the damage degree of the buildings, was also classified as five groups of no damage, slight damage, moderate damage, extensive damage, and complete damage. The ranges of input and output classification were obtained based on the supervised center classification (SCC-FCM) method in accordance with field data. [ABSTRACT FROM AUTHOR]

The change in the viscosity of a reaction mixture in the process of polystyrene synthesis and the possibility of controlling it in the course of polymerization have been studied. A laboratory reactor is used in this study, and the plots meeting our expectations are obtained for the change in the reactor stirrer rotation speed and output adjustable speed drive signal. An adequate fuzzy viscosity change model is created for use in the control system. [ABSTRACT FROM AUTHOR]

This paper makes a direct contribution to the mathematical theory and practical implementation of interpretable deep fuzzy learning whilst addressing the model interpretability challenge posed by the typical non-interpretable nature of training data. A deep autoencoder, composed of a finite number of stochastic fuzzy filters, is learned using variational Bayes for an efficient representation of high-dimensional feature vectors at different levels of abstraction. The robustness of the deep model towards data outliers is achieved by incorporating heavy-tailed distributed noises in the inference mechanism. The proposed deep stochastic fuzzy autoencoder makes it possible to have an interpretable classification by means of an induced mapping from a non-interpretable feature space to an interpretable parameter space. The mapping induced by our autoencoder can be used to explain the classifier's predictions by interpreting the non-interpretable high-dimensional feature vectors. The experimental results obtained on various benchmark datasets (e.g., Freiburg Groceries, Caltech-101, Caltech-256, Adobe Panoramas, and MNIST) show that our proposed approach not only outperforms widely used machine learning methods in classification tasks but brings the added value of interpretability as a further advantage. [ABSTRACT FROM AUTHOR]

• Compact UAV leveraging Coandă effect for exceptional maneuverability. • Exploring theoretical study, mechatronic design, and manufacturing complexities of Coandă UAV. • First application of Fuzzy Logic Controller to Coandă-effect UAV, surpassing PID control. • Advantageous Sugeno model provides faster, more robust control in uncertain environments. • Systematic comparison shows FLC strategy outperforms PID approach by 35.23% in settling time. • Comprehensive experimentation in Matlab® and Simulink for microcontroller-based UAV. • Groundbreaking fusion of novel design and advanced control techniques for Coandă UAVs. • Addressing unique aerodynamic challenges, contributing significantly to UAV research. Recent years have experienced a notable surge in unmanned aerial vehicles (UAV) research, prompting exploration into innovative concepts. This paper introduces a compact UAV harnessing the Coandă effect, an underexplored phenomenon in fluid mechanics. Featuring a single lift motor and two types of flaps, this UAV offers exceptional maneuverability, presenting significant challenges compared to conventional multi-rotor UAVs. To address these challenges, we explore the theoretical study, mechatronic design, and manufacturing complexities of the Coandă UAV. Emphasizing the distinctiveness of our work, we assess a Fuzzy Logic Controller (FLC) for UAV stabilization, marking the first application of such techniques to a Coandă-effect UAV, in contrast to the Proportional-Integral-Derivative (PID) control employed by other researchers. This innovative application of Fuzzy logic, particularly the Sugeno model, proves advantageous, offering faster and more robust control in uncertain or noisy environments. The proposed FLC strategy is systematically compared against a classical PID control approach, formulated based on the Mamdani and Sugeno models, optimized and manually tuned using a genetic algorithm. Our results showcase a significantly improved settling time of 0.417 s with the FLC strategy, surpassing the PID control approach by 35.23%. To substantiate our findings, we present comprehensive experimentation conducted at both software and hardware levels using Matlab® and Simulink for a microcontroller-based UAV. This groundbreaking fusion of novel design and advanced control techniques not only addresses the unique challenges posed by the Coandă UAV's aerodynamics but also contributes significantly to the field of UAV research. [ABSTRACT FROM AUTHOR]

Pre-crisis management planning is a matter of paramount importance which nowadays, urban managers have to deal with, especially in the filed of crisis management. In this research, finding locations for multi-purpose crisis management stations in regions 1, 4 and 10 of Tabriz municipality has been studied. At first step, the parameters affecting the selection of locations have been investigated. After gathering and preparing the layers, raster maps were obtained and subsequently, parameter weighing was done throughout the AHP process using Expertchoice software. At next step, data layers were combined together based on IO Boolean and Fuzzy models and the results were utilized to present some locations as desired onse, Due to the Geographic Information System in solving complex problems of urban and ease of analysis and spatial analysis , the ability of the system to prepare , collate and analyze the layers have been applied . The results obtained from this study, the southern part of the region which was set four as the best urban spaces such as green spaces, educational, sportive and etc, and better access conditions for the establishment of these bases is to have the highest priority and the best places for locating the base of the parks Amir Kabir, Anna, Tawhid and Jihad area around the field to 22 Bahman and a few other points is evaluated

Worn-Out urban textures have many problems in the physical, environmental, social and economic fields that have caused a lot of limitation for residents that live in these textures. In the presence of worn-out tissues, due to population growth and urbanization, natural disasters such as earthquakes can cause heavy losses and losses in such places. The need to reduce the vulnerability of cities and especially the worn-out structures against this phenomenon is considered as one of the main goals of urban planning and other related urban areas. Considering that the earthquake vulnerability is one of the most important challenges facing cities in Iran and is the subject of zoning of seismic risk and assessment of the vulnerability of cities to earthquakes in geographic and urban planning issues, this study attempts to measure the magnitude. The vulnerability of the corrosive tissues of Kashan city has been evaluated and solutions to this problem have been proposed. In this research, existing data from different organizations have been analyzed and evaluated using the fuzzy and fuzzy membership in the GIS environment. The results show that Kashan city with 754.3 hectare of worn out texture, located in different places of the city, the most used residential property with low quality, historical texture, ruined lands, long life of buildings and narrow and narrow streets exposed to greater vulnerability due to Earthquakes are more than other urban areas Among the worn-out tissues, the center of the city with the most and the Lather or lowest is the vulnerability.

Objective: The city of Kerman has seen many developments over the past decades. These developments include: physical development, urban population growth, inertial migration, marginalization and horizontal expansion of the city while maintaining the administrative, economic and cultural center of the South East, but building infrastructure, especially in the urban transport sector, in proportion to this developments have not been made. The present study, with the understanding of the importance of the subject and considering the position of Kerman city and the lack of coherent studies in this regard, it has been tried to investigate and analyze the dispersion of accidents in the city of Kerman by using GIS technology and fuzzy model and prioritize the accidental areas using the strategic planning to organize incidental urban areas. Methods: The type of research is operational and considering the nature of the subject, the research method is analytical-expository. The data assessment process is a combination of documentary and survey. Experts opinions and studies survey, show that in addition to cultural issues, the top ten factors affecting the rate of accidents, population density, density of accidents, density of service centers and passageways play the most role in the amount of accidents. Results:Based on the combination of effective factors and fuzzy method, three areas of Azadi Square, Khoyane Motahari and jihad are the most susceptible to intractable accidents, respectively. According to traffic flow projects in Azadi Square, Motahari Avenue (Motahari Park) has three ways Taleghani was designed to organize using strategic planning. Conclusion: The main purpose of this study was to emphasize the total number of accidents in order to describe accident locations and create a model based on the severity and variety of accidents. Conclusions show that central areas of the city, especially Azadi Square, Motahari Street, and Jihad Boulevard, were the most at risk of urban accidents.

Purpose. The development of a model for assessing the level of permissible risk of an emergency situation in the power system in the conditions of fuzzy input data, heterogeneity of the criteria for risk assessment, the absence of analytical links between them and the subjectivity of expert knowledge of decision makers. Methodology. To solve this problem, methods and models of fuzzy logic are used which give satisfactory analytical result in the conditions of uncertainty of the input information and the absence of analytical connections between the individual parameters and characteristics of the object. The proposed fuzzy models are constructed using the Fuzzy Output Algorithm of Mamdani. Findings. The result obtained by the fuzzy model gives the opportunity to assess reliably the acceptable level of risk of an emergency situation in the grid. Based on the magnitude of the risk, it is possible to make informed decisions regarding the expediency (or impracticability) of applying measures to reduce this risk. This enables the organization of preventive management of the risk of an emergency situation in the grid and the use of effective measures to reduce it. Originality. The article is devised a fuzzy model for estimating the permissible risk level of an emergency situation in the electric power system, which takes into account such criteria as the minimum possible level of risk, the influence of meteorological conditions and errors of operational and operational personnel of power systems. In this case, it is possible to take into account the main factors influencing the reliability of the grid functioning, to assess the risk level of an emergency situation.. Practical value. The fuzzy models developed in the article provide an opportunity to carry out an express assessment of the level of permissible risk of an emergency situation in the conditions of limited data and evaluation criteria, to conduct a comparative analysis of the obtained result with the magnitude of the actual risk of an accident in the on-line mode and to make a decision as to the appropriateness of its reduction.

Iran is a dry land with very little rainfall. The average rainfall in Iran is less than a third of the global average. Nowadays, with the increase in population and the subsequent increase in demand for food, the utilization of water resources, and in particular groundwater resources, is considerably increased. Currently the amount of groundwater resources exploitation exceeds its supply and so the lack of groundwater resources should be compensated. The purpose of this study was to locate zones suitable for the artificial recharge of the aquifers with a case study for Lorestan Province. For this purpose, AHP was employed as an efficient techniques and comprehensive system designed for multiple criteria decision-making. Required layers such as the slope, soil and geological structure, land use, rainfall, distribution of Quaternary alluvial and satellite imagery of the area was prepared. Then GIS software was used to overlay the layers and information and with use of Fuzzy logic, distinguishing suitable sites for artificial recharge of groundwater and the flood spreading. Based on the results, form the total area of ​​the province (28294 km²), about 9258.36 km² (32.5%) are suitable for the design of artificial recharge and 5926.36 km² (about 21%) can be considered for flood spreading. Finally, the integration of the two resulting maps led to suitable sites for artificial recharge and flood spreading as about 7682.21 km² (21.79%). Therefore, GIS models are a powerful tool to locate artificial recharge and flood plain sites, because of their ability for analysis of large geographical data and prioritization of scientific decisions.

With the advancement of the marketization process, inventory management has transformed from a single backup protection function to an essential function for enterprises, which helps to survive and develop. Inventory control in supply chain management is the important content of supply chain management. The new management mode makes inventory management present many new characteristics and problems compared with traditional inventory management. From the view of system theory and integration theory, it is imperative to re-examine the problem of inventory control, put forward new inventory management strategies adapted to integrated supply chain management, and improve the integration of the whole supply chain, which can enhance the agility and market response speed of enterprises. Based on the in-depth study of the joint inventory management model, this paper analyzed the current situation of the joint inventory management to optimize the inventory. In view of the achievements and shortcomings of the current research, a more systematic and improved optimization model of the supply chain inventory was proposed by using the basic ideas of ant colony algorithm and fuzzy model.

The departments of transportation worldwide are facing various challenges despite introducing and incorporating various vehicular features. One of such challenges is to make vehicles autonomous, intelligent, and capable of self-learning to evolve their knowledge repository. In this paper, human cognition is proposed to be implemented in vehicles so that they can perform human-like decisions. Therefore, the process of vehicular route decision is debated cognitively in order to provide route information intelligently. The in-vehicle routes provided by the GPS are not optimal and lack on-demand user requirements. GPS connectivity issues, in certain conditions, make it difficult for vehicles to take real-time decisions. This leads to the idea of self-decision by the vehicle controller. We propose a cognitive framework for vehicles to make self-decisions that use cognitive memory for storing route experiences. The framework strengthens the existing in-vehicle route finding capability and its provision in a more realistic manner. The user is provided with all available route-related information that is required for the journey. In addition, the route episodes are learned, stored, and accessed inside the cognitive memory for an optimal route provision. The vehicle learns about the routes and matures with route-experience by itself with the passage of time. In simulations, fuzzy modeling is used to validate the impact of cognitive parameters over static/conventional parameters. Moreover, artificial neural networks are used to minimize the error rate in learning to achieve cognitive route decisions. The proposed in-vehicle cognitive framework outperforms the existing route provision system that is inadequate and provokes the user's anxieties during driving. Besides, the proposed scheme gradually gets mature in delivering optimal as well as latest route-related information.

Background & Aim: One of the vital consequences of the expedite growth of urbanization and the physical development of the cities of the country has been the disruption of the distribution system of the service centers in recent decades, causing the social inequality of citizens in accessing such services. Urban public services structure the citychr('39')s physical, social and spatial nature; thus, irreparable effects are caused on the structure, the nature of the city and the class segregation of the urban neighborhoods due to injustice in its distribution, and faces urban management with serious challenges. The extension of the concepts of justice in the field of geography and urban literature, which began approximately 40 years ago, has recently led to new approaches in the field of epistemology. This study examines the spatial distribution of urban services in Mashhad metropolis with the aim of measuring spatial justice. Methodology: The present research is applied in terms of purpose and descriptive-analytical in terms of method. The data collection method is documentary and as a desk study and the technique used is a fuzzy method. Statistics and research indices were the distributions status of service use intervals at the level of 13 districts of Mashhad metropolis, obtained from a detailed plan. By measuring the research indices, the present research attempts to compare and classify the different districts of Mashhad in terms of the enjoyment rate of the access index, in order to provide a proper route for balancing the city development pillars. Findings: The analysis results indicated that approximately 10,381 hectares, equivalent to 30% of the city area suffers inadequate and relatively inadequate quality in accessing a variety of services, meanwhile districts 1, 11, and Samen were more appropriate than other districts. Also, districts 7, 6, and 2 lack the minimum zones with very good access to the services

The purpose of the study presented in the article was to develop a method for the formation of digital twins for distributed generation plants operating as part of cyber–physical power supply systems. A method of forming a digital twin for a system for automatic regulation of the voltage and rotor speed of a synchronous generator is considered. The structure of a digital twin is presented in the form of a multiply connected model using experimental data. The possibility of using a fuzzy inference system, artificial neural networks, and a genetic algorithm for solving the problem is shown. As a result of the research, neuro-fuzzy models of the elements of the distributed generation plant were obtained, which are an integral part of the digital twin. Based on the simulation results, the following conclusions were drawn: the proposed method for constructing an optimized fuzzy model gives acceptable results when compared with experimental data and shows practical applicability in constructing a digital twin. In the future, in order to simplify the model, it is necessary to solve the problem of optimizing the number of rules in the fuzzy inference system. It is also advisable to direct further research to the formation of a complete hierarchical fuzzy system that connects all elements of the digital twin.

The purpose of this paper is to present the results of a systematic literature review regarding the development of fuzzy-based models for time series forecasting in the period 2017–2021. The study was conducted using a well-established review protocol and a couple of powerful tools for bibliometric analysis to know and analyse the main approaches adopted in the research field of interest. We analysed 118 articles published in peer-reviewed journals indexed in the 2020 Journal Citation Reports of the Web of Science. This allowed us to present an in-depth performance analysis and a science mapping regarding the current situation of fuzzy time series forecasting and modelling. The outputs of this study provide a practical base for further investigations that address this topic from both a methodological point of view and in terms of applicability.

Activity recognition and classification are emerging fields of research that enable many human-centric applications in the sports domain. One of the most critical and challenged aspects of coaching is improving the performance of athletes. Hence, in this paper, the Adaptive Evolutionary Neuro-Fuzzy Inference System (AENFIS) has been proposed for sports person activity classification based on the biomedical signal, trial accelerator data and video surveillance. This paper obtains movement data and heart rate from the developed sensor module. This small sensor is patched onto the user’s chest to get physiological information. Based on the time and frequency domain features, this paper defines the fuzzy sets and assess the natural grouping of data via expectation-maximization of the probabilities. Sensor data feature selection and classification algorithms are applied, and a majority voting is utilized to choose the most representative features. The experimental results show that the proposed AENFIS model enhances accuracy ratio of 98.9%, prediction ratio of 98.5%, the precision ratio of 95.4, recall ratio of 96.7%, the performance ratio of 97.8%, an efficiency ratio of 98.1% and reduces the error rate of 10.2%, execution time 8.9% compared to other existing models. [ABSTRACT FROM AUTHOR]

In view of pregnancy at the uterine scar after cesarean section, there is an urgent need to find a conservative treatment mode with drugs that can improve the success rate of treatment, shorten the conservative treatment time, and cause little harm to the body. This research combines ultrasound intervention technology to construct a fuzzy model to comprehensively evaluate the clinical effects of traditional Chinese medicine combined with western medicine in the treatment of CSP. Moreover, this research combines the current application algorithms of machine vision to explain the relevant principles of ultrasound interventional vision. In addition, this research uses the background subtraction method to extract the target, and uses the machine vision algorithm to extract the ultrasonic detection target object. Finally, this research adjusts the control parameters according to the fuzzy control, and outputs the optimal control plan with BP network training. The results of this study show that the combination of Chinese and Western medicines in the treatment of uterine scars after cesarean section has the advantages of small gestational sacs, short pregnancy, and low blood β-HCG, and it has the advantages of less pain, economy, fewer side effects, and successfully preserves the uterus. Therefore, it is worthy of clinical application. [ABSTRACT FROM AUTHOR]

Music psychology can play an important role in the diagnosis and rehabilitation of mental health patients. Under the guidance of music psychology, this paper combines fuzzy models to process data, which effectively solves the problem that the number of categories in the fuzzy c-means algorithm needs to be manually given. The AFCC algorithm effectively combines the idea of semi-supervised clustering with the CA algorithm. Through two sets of must-link and cannot-link, this paper introduces the constraint penalty item into the objective function, which greatly improves the clustering accuracy. On this basis, this paper constructs a fuzzy model of psychological rehabilitation and diagnosis based on music psychology, designs experiments to verify the performance of this model, and conducts research results statistics from two aspects of diagnosis and rehabilitation. The research results show that the model constructed in this paper has certain practical effects. [ABSTRACT FROM AUTHOR]

A high concentration of colour, chemical oxidation demand (COD) and low biodegradability make textile wastewater a major source of pollution. While physicochemical processes, adsorption and chemical coagulation were the most commonly applied treatment methods for textile wastewater, the electrocoagulation process as compared to these conventional methods, produces less sludge with high colour removal efficiency. In this study, textile wastewater obtained from industry was treated by electrocoagulation process and the results were evaluated with fuzzy logic, an artificial intelligence-based modelling, which is used for advanced control and is employed in many environmental engineering applications, from wastewater treatment to air pollution estimation. Current density, initial pH and electrolysis time were selected as variables and utilized to predict COD, total organic carbon (TOC) and colour removal efficiencies. The experimental studies were applied to the developed fuzzy logic model to demonstrate the model accuracy. Using fuzzy logic model, surface maps were prepared for COD, TOC and colour removal. According to the obtained results, while Colour can be removed by 90%, COD and TOC can be removed by approximately 55 and 80%, respectively. While low pH was more efficient in COD and TOC removal, high pH values were shown to be efficient in colour removal. Time was the most efficient parameter for the electrocoagulation treatment and 15 minutes of reaction time was obtained as optimum value. Moreover, the results were evaluated with the multiple regression model. The obtained equations were maximized to determine the optimum conditions. These optimum conditions are 60 mA/cm2 of current density, pH 5 and 20 min of reaction time. When these parameters were examined, colour, TOC and COD removal efficiencies were attained as 89, 86 and 61%, respectively. [ABSTRACT FROM AUTHOR]

Predictive adaptive algorithms are powerful tools for controlling time varying systems. In particular, the Adaptive Generalized Predictive Control is widely used in different plants or processes. Nevertheless, a lack of robustness is often observed especially in case of large uncertainties in the model and in the presence of strong nonlinearities. Better solutions have been proposed using fuzzy modelling. In this paper, a direct adaptive version of the generalized predictive controller is developed. This allows an automatic online and real-time adjustment of local controller settings in order to maintain a proper level of performance. The contributions presented in this paper lie in the merging of local controllers using a fuzzy technique in order to produce a global controller using on-line parameters identification by a recursive lest squares type strategy. The stability of the closed-loop control system is demonstrated through the Lyapunov Stability Theory. Highly improved results are confirmed by simulation on an example of the commonly used Continuous Stirred Tank Reactor system. [ABSTRACT FROM AUTHOR]

In this article, a novel parallel tracking control optimization algorithm is first proposed for partially unknown fuzzy interconnected systems. In the existing standard optimal tracking control, the bounded or nonasymptotic stable reference trajectory will lead the feedback control not converging to zero, which causes the performance index infinite and invalid. By using the precompensation technique, in this article, the working feedback control is considered as a reconstructed dynamic with the virtual control and a new augmented fuzzy interconnected tracking system is built, thus that the performance index is valid for optimal control. Then, combining the integral reinforcement learning (RL) method and decentralized control design, the novel integral RL parallel algorithm is first developed to solve the tracking controls for interconnected systems, which relax the requirements of exact matrices information Aik and Bik during the solving process. Both the convergence and stability of the designed control optimization scheme are guaranteed by theorems. Finally, the new parallel tracking algorithm for interconnected systems is verified through the dual-manipulator coordination system and simulation results demonstrate the effectiveness. [ABSTRACT FROM AUTHOR]