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Road roughness significantly impacts vehicles' transportation performance. The purpose of this study is to develop an innovative, cost-effective, and precise method for estimating road roughness based on acceleration sensors. Unlike other approaches, this method employs a nonlinear full-vehicle dynamic model and a high-performance Gaussian nonlinear autoregressive with external inputs (G-NARX) neural network to significantly enhance the accuracy, without additional costs. In this study, acceleration sensors would first capture unsprung mass acceleration signals, and the neural network is trained with acceleration and velocity inputs. Then, the trained model would generate the root mean square value of the power spectral density to estimate the road roughness. Test results reveal that the G-NARX Inputs neural network outperforms the NARX neural network significantly, with the classification accuracy improved by at least 26% and the overall accuracy exceeding 90%. In road tests, the nonlinear model used in this study showed a 20% accuracy improvement over linear models. And field experiments conducted on three common types of roads further validated the robustness and accuracy of this study's estimation method. [ABSTRACT FROM AUTHOR]

Forecasting the price of cryptocurrencies is a notoriously hard and significant problem, due to the rapid market growth and high volatility. In this article, we propose a methodology for predicting future values of cryptocurrency exchange rates by developing a Non-linear Autoregressive with Exogenous Inputs (NARX) prediction model that uses the most adequate external information. The exogenous variables considered are historical values of the exchange rate and a series of technical indicators. The selection of the most relevant external inputs is based on the computation of the mutual information indicator and estimated using the k-nearest neighbor method. The methodology employs a fine-tuned Long Short-Term Memory (LSTM) neural network as the regressor. We have used quantitative and trend accuracy measures to compare the proposed method against other state-of-the-art LSTM-based models. In addition, regarding the input selection process, the proposed approach was compared against the most commonly used one, which is based on the cross-correlation coefficient. A long series of experiments and statistical analyses proved that the proposed methodology is highly accurate and the resulting model outperforms the state-of-the-art LSTM-based models. [ABSTRACT FROM AUTHOR]

Motion control of modern and advanced aircraft has to be provided under conditions of incomplete and inaccurate knowledge of their parameters and characteristics, possible flight modes, and environmental influences. In addition, various abnormal situations may occur during flight, in particular, equipment failures and structural damage. These circumstances cause the problem of a rapid adjustment of the used control laws so that the control system can adapt to the mentioned changes. However, most adaptive control schemes have a model of the control object, which plays a crucial role in adjusting the control law. That is, it is required to solve also the identification problem for dynamical systems. We propose an approach to solving the above-mentioned problems based on artificial neural networks (ANNs) and hybrid technologies. In the class of traditional neural network technologies, we use recurrent neural networks of the NARX type, which allow us to obtain black-box models for controlled dynamical systems. It is shown that in a number of cases, in particular, for control objects with complicated dynamic properties, this approach turns out to be inefficient. One of the possible alternatives to this approach, investigated in the paper, consists of the transition to hybrid neural network models of the gray box type. These are semi-empirical models that combine in the resulting network structure both empirical data on the behavior of an object and theoretical knowledge about its nature. They allow solving with high accuracy the problems inaccessible by the level of complexity for ANN models of the black-box type. However, the process of forming such models requires a very large consumption of computational resources. For this reason, the paper considers another variant of the hybrid ANN model. In it, the hybrid model consists not of the combination of empirical and theoretical elements, resulting in a recurrent network of a special kind, but of the combination of elements of feedforward networks and recurrent networks. Such a variant opens up the possibility of involving deep learning technology in the construction of motion models for controlled systems. As a result of this study, data were obtained that allow us to evaluate the effectiveness of two variants of hybrid neural networks, which can be used to solve problems of modeling, identification, and control of aircraft. The capabilities and limitations of these variants are demonstrated on several examples. Namely, on the example of the problem of aircraft longitudinal angular motion, the possibilities of modeling the motion using the NARX network as applied to a supersonic transport aircraft (SST) are first considered. It is shown that under complicated operating conditions this network does not always provide acceptable modeling accuracy. Further, the same problem, but applied to a maneuverable aircraft, as a more complex object of modeling and identification, is solved using both a NARX network (black box) and a semi-empirical model (gray box). The significant advantage of the gray box model over the black box one is shown. The capabilities of the hybrid model realizing deep learning technologies are demonstrated by forming a model of the control object (SST) and neurocontroller on the example of the MRAC adaptive control scheme. The efficiency of the obtained solution is illustrated by comparing the response of the control object with a failure situation (a decrease in the efficiency of longitudinal control by 50%) with and without adaptation. [ABSTRACT FROM AUTHOR]

This study aims to predict the number of accidents in the National Iranian Oil Products Distribution Company (NIOPDC) as a case study in 2022 according to the database between 2012 and 2021. Artificial Neural Network (ANN) is used for modeling using curve fitting (Multi-Layer Perceptron-MLP) and time series (Nonlinear AutoRegressive exogenous -NARX) networks. The network parameters are adjusted by optimal architecture values to create a successful model with coefficient determination (R2) and Mean Square Error (MSE) performance criteria. Also, mathematical methods of Root Mean Square Error (RMSE), Average Invalidity Percentages (AIP), Average Validity Percentages (AVP), and Mean Absolute Error (MAE) are checked out to evaluate the proposed model's robustness. The results show acceptable R2 values of 0.98 and 0.99 for MLP and NARX networks, respectively, demonstrating that NARX has more prediction accuracy than the MLP network. By the best model (NARX) results, falls, CNG, and LPG facility accidents will be half. Nevertheless, the aviation center and loading rack accidents will rise 3 and 1.5 times, respectively. The findings will be helpful in systematic accident prevention for decision-making authorities which have been done in the Iranian petroleum industry for the first time. [ABSTRACT FROM AUTHOR]

The power generation by solar photovoltaic (PV) systems will become an important and reliable source in the future. Therefore, this aspect has received great attention from researchers, who have investigated accurate and credible models to predict the power output of PV modules. This prediction is very important in the planning of short-term resources, the management of energy distribution, and the operation security for PV systems. This paper aims to explore the sensitivity of Nonlinear Autoregressive Exogenous Inputs (NARX) and an Artificial Neural Network (ANNs) as a result of weather dynamics in the very short term for predicting the power output of PV modules. This goal was achieved based on an experimental dataset for the power output of a PV module obtained during the sunny days in summer and cloudy days in winter, and using the data in the algorithm models of NARX and ANN. In addition, the analysis results of the NARX model were compared with those of the static ANN model to measure the accuracy and superiority of the nonlinear model. The results showed that the NARX model offers very good estimates and is efficient in predicting the power output of the PV module in the very short term. Thus, the coefficient of determination (R2) and mean square error (MSE) were 94.4–97.9% and 0.08261–0.04613, respectively, during the summer days, and the R2 and MSE were 90.1–89.2% and 0.281–0.249, respectively, during the winter days. Overall, it can be concluded that the sensitivity of the NARX model is more accurate in the summer days than the winter days, when the weather conditions are more stable with a gradual change. Moreover, the effectiveness of the NARX model has the specificity to learn and to generalize more effectively than the static ANN. [ABSTRACT FROM AUTHOR]

This article discusses the development of an enhanced monitoring and control system for helicopter turboshaft engines during flight operations, leveraging advanced neural network techniques. The research involves a comprehensive mathematical model that effectively simulates various failure scenarios, including single and cascading failure, such as disconnections of gas-generator rotor sensors. The model employs differential equations to incorporate time-varying coefficients and account for external disturbances, ensuring accurate representation of engine behavior under different operational conditions. This study validates the NARX neural network architecture with a backpropagation training algorithm, achieving 99.3% accuracy in fault detection. A comparative analysis of the genetic algorithms indicates that the proposed algorithm outperforms others by 4.19% in accuracy and exhibits superior performance metrics, including a lower loss. Hardware-in-the-loop simulations in Matlab Simulink confirm the effectiveness of the model, showing average errors of 1.04% and 2.58% at 15 °C and 24 °C, respectively, with high precision (0.987), recall (1.0), F1-score (0.993), and an AUC of 0.874. However, the model's accuracy is sensitive to environmental conditions, and further optimization is needed to improve computational efficiency and generalizability. Future research should focus on enhancing model adaptability and validating performance in real-world scenarios. [ABSTRACT FROM AUTHOR]

Applying artificial neural network techniques to forecast the electricity production of photovoltaic (PV) power plants is a novel concept. A reliable analytical model for calculating the energy output of a grid-connected solar plant is very difficult to establish because of hourly, daily, and seasonal variations in climate. The current study estimated and predicted the energy production of a connected PV system that was installed in Cairo, Egypt (30.13° N and 31.40 ° E) using an artificial neural network. Four seasons' worth of data (summer, autumn, winter, and spring) were methodically assessed using information from the climate database. The parameters that had an impact on the electrical data of PV modules included meteorological and irradiation variables, energy output, and the user's needs used to verify the NARX feedback neural networks. Prediction performance metrics were obtained, such as the correlation coefficient (R) and root mean square error (RMSE). The observed correlation coefficient ranged from 99% to 100%, indicating that the expected results are verified, while the mean error fluctuates very little. [ABSTRACT FROM AUTHOR]

The combinations of chemical admixtures play a significant role in producing concrete. Understanding their mechanical properties is crucial for ensuring safety and durability. Among these properties, compressive strength (CS) stands out as the most critical attribute of concrete. This research introduced a distinct concrete mix utilizing a blend of superplasticizers, retarders, and air-entraining agents, designed to meet specific construction requirements for enhanced durability and workability. The study examines the long-term effects of combining various chemical admixtures on the compressive strength of concrete, utilizing advanced experimental data and machine learning models with a level of precision and detail that has been relatively underexplored.This investigation includes a substantial increase in samples (7845) compared to previous research. Samples were tested at different ages, ranging from 3 days to 3 years. To enhance the accuracy of machine learning (ML) models, a novel approach to data distribution simulation based on K-means clustering was employed for generating synthetic data. Various ML models, including Nonlinear Autoregressive with Exogenous Inputs (NARX), Support Vector Regression (SVR), Radial Basis Function (RBF), Multilayer Perceptron (MLP), Decision Tree (DT), and Random Forest (RF), were evaluated for predicting the compressive strength of concrete (CS). Results show the NARX model outperforms the other models, validated by experimental data and k-fold cross-validation. This model showed a coefficient of determination (R2 = 0.9932), normalized mean square error (NMSE = 18.97), normalized mean absolute error (NMAE = 2.49), and normalized root-mean-square error (NRMSE = 6.18). The findings revealed that in Mix - 1, the compressive strength increased from 450 (kg/cm2) at 28 days to 480 (kg/cm2)at 90 days, but then decreased to 420 (kg/cm2) after three years. This reduction in strength may lead to decreased load-bearing capacity and higher repair costs, highlighting the need to revise concrete design standards. This study emphasizes revising some current concrete structure design standards to accommodate the observed long-term reductions in compressive strength.

This paper reports the prediction results of the ionospheric irregularities represented by the ROTI index close to the equatorial ionization anomaly's Northern peak. A feedforward backpropagation Neural Network (NN) approach is implemented using a time series Nonlinear Autoregressive approach with Exogenous inputs (NARX). We used the data from a dual frequency GPS-SCINDA station located at Helwan (geographic coordinates of 29.86°N, 31.32°E, and MLAT of 29.94°N) in Egypt. To allow the model to be independently tested during varying levels of solar activities, we collected a 5-minute resolution of ROTI data for the solar cycle 24 from 2009 to 2017. The factors that influence the development of ionospheric irregularities are involved in the established model representing the diurnal and seasonal variations as well as solar and geomagnetic activity parameters. To support learning, we included IRI-foF2 and IRI-hmF2 parameters in the input layer neurons to improve the model learning about the behavior of the ionospheric F layer. The results show that the NN-ROTI values precisely match the GPS-ROTI values with an RMSE of 0.106 TECU/min and a prediction efficiency of 95%. The predicted values are highly correlated with the originals, with a regression of 0.89. Furthermore, the irregularities were more prevalent during the equinox months than during the solstice months. It is also observed that predicted NN-ROTI values in all seasons have RMSEs less than 0.03 and 0.05 TECU/min for the low and high solar activity, respectively. The results also showed a clear correlation between solar activity and the occurrence percentage of ionospheric irregularities indicating a solar cycle dependency. The prediction values of the NN-ROTI occurrence % demonstrated a remarkable correlation with the observed GPS-ROTI occurrence % across SC24. [ABSTRACT FROM AUTHOR]

The increasing global reliance on renewable energy sources, particularly solar energy, underscores the critical importance of accurate solar irradiance forecasting. As solar capacity continues to grow, precise predictions of solar irradiance become essential for optimizing the performance and reliability of photovoltaic (PV) systems. This study introduces a novel hybrid forecasting model that integrates Nonlinear Autoregressive with Exogenous Inputs (NARX) with Long Short-Term Memory (LSTM) networks. The purpose is to enhance the precision of predicting daily solar irradiance in fluctuating meteorological scenarios, particularly in southwestern France. The hybrid model employs the NARX model's capacity to handle complex non-linear relationships and the LSTM's aptitude to manage long-term dependencies in time-series data. The performance metrics of the hybrid NARX-LSTM model were thoroughly assessed, revealing a mean absolute error (MAE) of 9.58 W/m2, a root mean square error (RMSE) of 16.30 W/m2, and a Coefficient of Determination (R2) of 0.997. Consequently, the proposed hybrid model outperforms the benchmark model in all metrics, showing a significant improvement in prediction accuracy and better alignment with the observed data. These results highlight the model's effectiveness in enhancing forecasting accuracy under unpredictable conditions, improving solar energy integration into power systems, and ensuring more reliable energy predictions. [ABSTRACT FROM AUTHOR]

Accurate power load forecasting is critical to achieving the sustainability of energy management systems. However, conventional prediction methods suffer from low precision and stability because of crude modules for predicting short-term and medium-term loads. To solve such a problem, a Combined Modeling Power Load-Forecasting (CMPLF) method is proposed in this work. The CMPLF comprises two modules to deal with short-term and medium-term load forecasting, respectively. Each module consists of four essential parts including initial forecasting, decomposition and denoising, nonlinear optimization, and evaluation. Especially, to break through bottlenecks in hierarchical model optimization, we effectively fuse the Nonlinear Autoregressive model with Exogenous Inputs (NARX) and Long-Short Term Memory (LSTM) networks into the Autoregressive Integrated Moving Average (ARIMA) model. The experiment results based on real-world datasets from Queensland and China mainland show that our CMPLF has significant performance superiority compared with the state-of-the-art (SOTA) methods. CMPLF achieves a goodness-of-fit value of 97.174% in short-term load prediction and 97.162% in medium-term prediction. Our approach will be of great significance in promoting the sustainable development of smart cities. [ABSTRACT FROM AUTHOR]

Physically based or data-driven models can be used for understanding basinwide hydrological processes and creating predictions for future conditions. Physically based models use physical laws and principles to represent hydrological processes. In contrast, data-driven models focus on input–output relationships. Although both approaches have found applications in hydrology, studies that compare these approaches are still limited for data-scarce, semi-arid basins with altered hydrological regimes. This study aims to compare the performances of a physically based model (Soil and Water Assessment Tool (SWAT)) and a data-driven model (Nonlinear AutoRegressive eXogenous model (NARX)) for reservoir volume and streamflow prediction in a data-scarce semi-arid region. The study was conducted in the Tersakan Basin, a semi-arid agricultural basin in Türkiye, where the basin hydrology was significantly altered due to reservoirs (Ladik and Yedikir Reservoir) constructed for irrigation purposes. The models were calibrated and validated for streamflow and reservoir volumes. The results show that (1) NARX performed better in the prediction of water volumes of Ladik and Yedikir Reservoirs and streamflow at the basin outlet than SWAT (2). The SWAT and NARX models both provided the best performance when predicting water volumes at the Ladik reservoir. Both models provided the second best performance during the prediction of water volumes at the Yedikir reservoir. The model performances were the lowest for prediction of streamflow at the basin outlet (3). Comparison of physically based and data-driven models is challenging due to their different characteristics and input data requirements. In this study, the data-driven model provided higher performance than the physically based model. However, input data used for establishing the physically based model had several uncertainties, which may be responsible for the lower performance. Data-driven models can provide alternatives to physically-based models under data-scarce conditions. [ABSTRACT FROM AUTHOR]

The advancements in remote sensing (RS) satellite applications have revolutionized natural disaster surveillance and prediction in the earthquake monitoring by delineating various precursors at the Earth's surface and in atmosphere. In this paper, the earthquake precursors comprising land surface temperature, outgoing longwave radiations, relative humidity, and air temperature for both the daytime and nighttime are investigated for two Mw > 6.0 events in USA. Interestingly, we noticed surface and atmospheric parameters anomalies in 6–8 days window prior to both the events by using standard deviation method. Moreover, these abrupt deviations are also validated by the recurrent neural networks like autoregressive network with exogenous inputs and long short-term memory inputs. The findings of this study demonstrate the potential of using modern analysis tools to further develop our knowledge of the linked dynamics of the lithosphere and atmosphere preceding seismic occurrences. This study implements substantially the developing of natural hazard surveillance and earthquake prediction capabilities for future researches as a valuable addition of reference in the field of RS. [ABSTRACT FROM AUTHOR]

Motion control of modern and advanced aircraft has to be provided under conditions of incomplete and inaccurate knowledge of their parameters and characteristics, possible flight modes, and environmental influences. In addition, various abnormal situations may occur during flight, in particular, equipment failures and structural damage. These circumstances cause the problem of a rapid adjustment of the used control laws so that the control system can adapt to the mentioned changes. However, most adaptive control schemes have a model of the control object, which plays a crucial role in adjusting the control law. That is, it is required to solve also the identification problem for dynamical systems. We propose an approach to solving the above-mentioned problems based on artificial neural networks (ANNs) and hybrid technologies. In the class of traditional neural network technologies, we use recurrent neural networks of the NARX type, which allow us to obtain black-box models for controlled dynamical systems. It is shown that in a number of cases, in particular, for control objects with complicated dynamic properties, this approach turns out to be inefficient. One of the possible alternatives to this approach, investigated in the paper, consists of the transition to hybrid neural network models of the gray box type. These are semi-empirical models that combine in the resulting network structure both empirical data on the behavior of an object and theoretical knowledge about its nature. They allow solving with high accuracy the problems inaccessible by the level of complexity for ANN models of the black-box type. However, the process of forming such models requires a very large consumption of computational resources. For this reason, the paper considers another variant of the hybrid ANN model. In it, the hybrid model consists not of the combination of empirical and theoretical elements, resulting in a recurrent network of a special kind, but of the combination of elements of feedforward networks and recurrent networks. Such a variant opens up the possibility of involving deep learning technology in the construction of motion models for controlled systems. As a result of this study, data were obtained that allow us to evaluate the effectiveness of two variants of hybrid neural networks, which can be used to solve problems of modeling, identification, and control of aircraft. The capabilities and limitations of these variants are demonstrated on several examples. Namely, on the example of the problem of aircraft longitudinal angular motion, the possibilities of modeling the motion using the NARX network as applied to a supersonic transport aircraft (SST) are first considered. It is shown that under complicated operating conditions this network does not always provide acceptable modeling accuracy. Further, the same problem, but applied to a maneuverable aircraft, as a more complex object of modeling and identification, is solved using both a NARX network (black box) and a semi-empirical model (gray box). The significant advantage of the gray box model over the black box one is shown. The capabilities of the hybrid model realizing deep learning technologies are demonstrated by forming a model of the control object (SST) and neurocontroller on the example of the MRAC adaptive control scheme. The efficiency of the obtained solution is illustrated by comparing the response of the control object with a failure situation (a decrease in the efficiency of longitudinal control by 50%) with and without adaptation.

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Accurate forecasting of airline passenger traffic is important for facilitating the effective management and planning of aviation resources. In this study, we explore the air passenger traffic in the Norwegian aviation industry by collecting the passenger flow data and the corresponding measurements of the weather conditions affecting the flow from the different airports in Norway. We then proposed nonlinear autoregressive with exogenous input (NARX) forecasting models to predict air passenger traffic in advance. The NARX models account for the nonlinearity and nonstationarity in the passenger flow and allow the accurate forecasting of air passenger traffic. We perform experiments to demonstrate the effectiveness of two variants of the NARX model and compare their performances against long short‐term memory (LSTM), a deep learning method. We show that the proposed NARX model achieves the best prediction accuracy compared to LSTM, which is considered as a state‐of‐the‐art approach for fitting sequential data. [ABSTRACT FROM AUTHOR]

An identification technique is proposed to create a relation between the accelerator pedal position and the corresponding driving moment. This step is beneficial to replace the complex physical model of the vehicle control unit, especially when the sufficient information needed to model certain functionalities of the vehicle control unit are unavailable. We utilized the nonlinear autoregressive exogenous model to regenerate the electric motor torque demand, given the accelerator pedal position, the motor's angular speed, and the vehicle's speed. This model proved to be extremely efficient in representing this highly complex relationship. The data employed for the identification process were chosen from an actual three-dimensional route with sudden changes of a dynamic nature in the driving mode, different speed limits, and elevations, as an attempt to thoroughly cover the driving moment scope based on the alternation of the given inputs. Analyzing the selected route data points showed the widespread coverage of the motor's operational scope compared to a standard driving cycle. The training outcome revealed that linear modeling is inadequate for identifying the targeted system, and has a substantial estimation error. Adding the nonlinearity feature to the model led to an exceptionally high accuracy for the estimation and validation datasets. The main finding of this work is that the combined model from the nonlinear autoregressive exogenous and the sigmoid network enables the accurate modeling of highly nonlinear dynamic systems. Accordingly, the maximum absolute estimation error for the motor's moment was less than 10 Nm during the real-world driving maneuver. The highest errors are found around the maximum motor's moment. Finally, the model is validated with measurements from an actual field test maneuver. The identified model predicted the driving moment with a correlation of 0.994. [ABSTRACT FROM AUTHOR]

The paper involved conducting preliminary research to explore the identification and control of a multi-dimensional, non-linear, and non-stationary cement grinding process using artificial neural networks and various other non-linear models. The primary objective was to establish a precise model that accurately characterizes the functioning of the grinding system. Several model structures were employed, including NARX models based on feed-forward network, Elman, Jordan, and Layer-Recurrent Network (LRN) recurrent networks, as well as MTL (Multi-Task Learning) and traditional NARX non-linear models. It was observed that, in contrast to the linear models, the non-linear models exhibited significantly superior performance in the modeling of the system. Another notable outcome of this research is the proposal of a neurocontroller, functioning as an expert system, which can provide control signals to operators. The development and implementation of such a neurocontroller have the potential to enhance the quality, simplicity, and efficiency of cement grinding process control.

We consider the challenge of detecting and clustering point and collective anomalies in streaming data that exhibit significant nonlinearities and seasonal structures. The challenge is motivated by detecting problems in a communications network, where we can measure the throughput of nodes, and wish to rapidly detect anomalous traffic behaviour. Our approach is to train a neural network-based nonlinear autoregressive exogenous model on initial training data, then to use the sequential collective and point anomaly framework to identify anomalies in the residuals generated by comparing one-step-ahead predictions of the fitted model with the observations, and finally, we cluster the detected anomalies with fuzzy c-means clustering using empirical cumulative distribution functions. The autoregressive model is sufficiently general and robust such that it provides the nearly (locally) stationary residuals required by the anomaly detection procedure. The combined methods are successfully implemented to create an adaptive, robust, computational framework that can be used to cluster point and collective anomalies in streaming data. We validate the method on both data from the core of the UK’s national communications network and the multivariate Skoltech anomaly benchmark and find that the proposed method succeeds in dealing with different forms of anomalies within the nonlinear signals and outperforms conventional methods for anomaly detection and clustering.

The wide-ranging use of renewable energy sources (RES) in the modern era leads to the association of distributed non-conventional energy resources in contrary to large-scale and localized conventional non-renewable sources. The prevalent use of distributed sources produces various Power-Quality issues. A thorough review of the literature provides numerous approaches for Power Quality (PQ) improvements; however, most techniques can improve only a few PQ problems. The new technical challenges like power loss, stability, reliability and power quality (PQ) issues are observed in present micro grid scenario. In this regard, this research work is an attempt to address PQ improvement in a three-phase realistic complex micro grid (integration of three simple micro grids to the point of common coupling. Improvement of various PQ factors for example sag/swell, unbalancing, power factor (Pf), total harmonic distortion (THD), communication delay and addition of impedance has been addressed in this research work. In this research article, a Kernel based Deep Auto Regressive Exogenous Output Neural Network (KDNARX) controller has been proposed and applied to improve power quality issues in a complex or realistic three phase complex micro grid (RTPCMG) in grid connected mode of operation. The RTPCMG is a combination of three simple grids with individual ratings of (4 kW, 4 kW and 6 kW). Photo voltaic cell, wind generator (WG), fuel cell (FC) and battery energy storage system (BESS) are the constituent distributed generators (DGs) for the proposed micro grid. The PQ improvement by application of proposed KDNARX controller to the DG inverter switching, has been overly tested in 12 different power quality issues under various operating conditions. The Kernel parameters are optimized by a new optimization technique called Cosine Chaotic PSO (CCPSO) algorithm. The results proved the strength of the new control technique ensuring PQ improvement and stability study of a three phase AC micro grid. Also, the potentiality of the new control scheme is compared with ANN and NARX and its superiority has been proved. Further, some case studies are validated through hardware in loop (HIL) environment thus justifying its real time implementation.

This article discusses the development of an enhanced monitoring and control system for helicopter turboshaft engines during flight operations, leveraging advanced neural network techniques. The research involves a comprehensive mathematical model that effectively simulates various failure scenarios, including single and cascading failure, such as disconnections of gas-generator rotor sensors. The model employs differential equations to incorporate time-varying coefficients and account for external disturbances, ensuring accurate representation of engine behavior under different operational conditions. This study validates the NARX neural network architecture with a backpropagation training algorithm, achieving 99.3% accuracy in fault detection. A comparative analysis of the genetic algorithms indicates that the proposed algorithm outperforms others by 4.19% in accuracy and exhibits superior performance metrics, including a lower loss. Hardware-in-the-loop simulations in Matlab Simulink confirm the effectiveness of the model, showing average errors of 1.04% and 2.58% at 15 °C and 24 °C, respectively, with high precision (0.987), recall (1.0), F1-score (0.993), and an AUC of 0.874. However, the model’s accuracy is sensitive to environmental conditions, and further optimization is needed to improve computational efficiency and generalizability. Future research should focus on enhancing model adaptability and validating performance in real-world scenarios.

Constructing the reliable dynamic sensitivity profile for the output variable using the machine learning model is a challenging task; however, the dynamic sensitivity trends are helpful to understand the impact of the input variables on the system's performance. In this paper, we have derived the partial-derivative approach-based sensitivity analysis expression for the non-linear auto regressive with exogenous (NARX) model for the first time. The engineering systems-based case studies, i.e., two distillation columns with five and ten stages, respectively are taken which are commonly found in the chemical processing plants. Two output variables, i.e., liquid composition in tray 2 and tray 4 (Y2 and Y4) of a five-stage distillation column, and liquid composition in tray 7 (Y7) of a ten-stage (higher) distillation column are modelled by NARX with respect to time, feed concentration (Xf) and feed flow rate (Lf). The dynamic sensitivity profiles of the output variables with respect to Xf and Lf for the two distillation columns are plotted by the derived partial derivative-based sensitivity expression on the NARX model. Furthermore, the forward difference method of sensitivity analysis (first principle method) is also applied on the ordinary differential equations of the distillation columns to compute the sensitivity values of the output variables. A good agreement in the dynamic sensitivity values of the output variables with respect to the input variables is found for the two sensitivity analysis techniques thereby demonstrating the effectiveness of the partial-derivative approach for the improved NARX's interpretability performance. This research presents the explicit partial-derivative based sensitivity analysis expression for the NARX model which can be utilised for time-series applications and can provide the insights about the model's interpretation performance.

This article presents research on the model of forecasting the average daily air pollution levels focused mainly on two solutions, artificial neural networks: the NARX model and the LSTM model. The research used an air quality monitoring system. This system includes individually designed and implemented sensors to measure the concentration of pollutants such as PM10, PM2.5, SO2, NO2 and to record weather conditions such as temperature, humidity, pressure, wind strength and speed. Data is sent to a central database server based on the MQTT protocol. Additional weather information in the area covered by pollution monitoring is collected from the weather services of the IMGW and openwethermap.org. The artificial neural network models were built in the MATLAB environment, the process of learning neural networks was performed and the results of pollution prediction for the level of PM10 dust were tested. The models showed good and acceptable results when forecasting the state of PM10 dust concentration in the next 24 hours. The LSTM prediction model were more accurate than the NARX model. The future work will be related to the use of artificial intelligence algorithms to predict the concentration of other harmful substances, e.g. PM2.5, NO2, SO2 etc. A very important task in the future will be to frame the entire system of monitoring and predicting smog in a given area.

Water temperature is an important physical indicator of rivers because it impacts many other physical and biogeochemical processes and controls the metabolism of aquatic species in rivers. Having a good knowledge of river thermal dynamics is of great importance. In this study, an advanced machine learning based model that is fast, accurate and easy to use, namely the nonlinear autoregressive network with exogenous inputs (NARX) neural network, was coupled with Bayesian Optimization (BO) algorithm for optimizing the number of NARX hidden nodes and lagged input/target values and the Bayesian Regularization (BR) backpropagation algorithm for the NARX training, to forecast daily river water temperatures (RWT). Long-term observed data from 18 rivers of the Vistula River Basin, one of the largest rivers in Europe, were used for model testing, and model performance was compared with the air2stream model. The results showed that the NARX-based model performs significantly better than the air2stream model in the calibration and validation stages, and can better capture the seasonal pattern and peak values of RWT. Input combinations impact the performance of the NARX-based model in RWT modeling, and air temperature and the day of the year (DOY) are the major inputs, while streamflow and rainfall play a minor role on modeling RWT at the Vistula River Basin. Considering that future times series of air temperatures are easily accessible from climate models and DOY is easy to be considered in the model, the NARX-based model can serve as a promising tool to investigate the impact of climate change on river thermal dynamics.

A decentralized intelligent droop-based control strategy is proposed in this paper for the enhancement of equal active and reactive power sharing in an islanded inverter-based microgrid. Droop control is mostly preferred because it does not need communication facilities for its implementation. However, the presence of different feeder impedances for the different distributed generators (DGs) in a microgrid make reactive power sharing inaccurate. Furthermore, as a result of considerable load changes and different droop characteristics for the DGs, the inverter's output voltage and frequency are altered and these in turn alter the reactive and active power sharing respectively. With these conditions, the generalized droop control (GDC) strategy fails to effectively share load reactive and active power among the DGs. In order to extenuate these challenges, this paper presents a nonlinear autoregressive exogenous neural network droop-based control (NARX-NN DBC) strategy which does not depend on the varying line impedances, droop gains for the different DGs and is less affected by fluctuating loads in the grid. A microgrid, made up of two DGs and a load is modelled in MATLAB/Simulink environment and validation of the proposed control strategy is done through many simulations. Within the simulation runtime, NARX-NN DBC yielded a maximum frequency percentage deviation of 0.46% from the nominal value of 50 Hz whereas GDC yielded 0.62%. Regarding the voltage, NARX-NN DBC gave maximum deviation of 0.026% meanwhile GDC gave 0.079% from the nominal value for 380 V. In addition, during 0.57–0.64 s with load active power demand of 4.6 kW, NARX-NN DBC registered 0.43% power sharing error whereas GDC registered 6.5%. On the other hand, during 0.57–0.64 s, with 5kVAr load power demand, NARX-NN DBC registered 0.2% whereas, GDC registered 2%. These obtained results clearly show that NARX-NN DBC strategy has a better performance compared to GDC strategy with respect to power sharing in an autonomous microgrid.

The integration of micro-electro-mechanical system–inertial navigation systems (MEMS-INSs) with other autonomous navigation sensors, such as polarization compasses (PCs) and geomagnetic compasses, has been widely used to improve the navigation accuracy and reliability of vehicles in Internet of Things (IoT) applications. However, a MEMS-INS/PC integrated navigation system suffers from cumulative errors and time-varying measurement noise covariance in unknown, complex occlusion, and dynamic environments. To overcome these problems and improve the integrated navigation system's performance, a dual data- and model-driven MEMS-INS/PC seamless navigation method is proposed. This system uses a nonlinear autoregressive neural network (NARX) based on the Gauss–Newton Bayesian regularization training algorithm to model the relationship between the MEMS-INS outputs composed of the specific force and angular velocity data and the PC heading's angular increment, and to fit the integrated navigation system's dynamic characteristics, thus realizing data-driven operation. In the model-driven part, a nonlinear MEMS-INS/PC loosely coupled navigation model is established, the variational Bayesian method is used to estimate the time-varying measurement noise covariance, and the cubature Kalman filter method is then used to solve the nonlinear problem in the model. The robustness and effectiveness of the proposed method are verified experimentally. The experimental results show that the proposed method can provide high-precision heading information stably in complex, occluded, and dynamic environments. [ABSTRACT FROM AUTHOR]

The application of machine learning (ML) in the prediction of hydrogen (H2) production has proven to be an efficient tool for enhancing production capacity. However, while the reported algorithms have demonstrated robustness in hydrogen prediction, they fall short in capturing the dynamic nature of real-time production uncertainty. In this study, we proposed a dynamic data-driven algorithm for the identification and prediction of H2 gas during an experimental steam gasification process. Initially, feature selection was conducted to assess the appropriateness of the data obtained from literature. Subsequently, a machine learning model, specifically based on the time series recurrent artificial neural network architecture, was developed to forecast the H2 yield using predefined model inputs. To establish a benchmark for comparison, the machine learning model was optimized using the genetic algorithm (GA) optimizer to evaluate its predictive performance. The findings of the investigation revealed that the optimized NARx model exhibits robustness in predicting H2 yields, achieving an R2 value exceeding 0.90 and a Mean Squared Error (MSE) below 0.070, when evaluated against a predefined time trajectory. This stands in contrast to the performance of the reference NARx model, which yielded an R2 below 0.90 and an MSE exceeding 0.40. This model could effectively be used to develop decision making strategy for large scale H2 production from biomass gasification process. [ABSTRACT FROM AUTHOR]

The electric vehicle battery state prediction in real time is an important issue to avoid the risks of burning the battery due to over-charging or dead batteries that are caused by aging. Based on the past works, it is found that the State of Charge (SOC) can be predicted, while predicting the State of Health (SOH) is a difficult challenge. Usually, the SOH is predicted after the end of the driving or the charging cycle under constant conditions; this method is practically impossible because the battery can reach the end of the battery life before achieving the prediction process. In this paper, a SOH prediction method is proposed based on SOC prediction because there is a relation between the SOC and the SOH as indicated by deriving a mathematical model. The prediction process of battery age is achieved during the beginning of the battery charging process under constant conditions of charging, in which a SOC estimation has been implemented by the nonlinear auto-regressive with exogenous input neural network (NARX) with two initial values of SOC, the default value (0%) and practical value (10%) and two charging current rates (0.5C and 1C). The proposed method has been simulated by MATLAB, which several scenarios have been achieved to validate the proposed method. The root-mean-square error (RMSE) values are very promising for both predicting SOC and SOH that are 0.5% and 0.018%, respectively. [ABSTRACT FROM AUTHOR]

In any solar adsorption refrigeration system, there are three major components: a solar collector adsorbent bed, a condenser and an evaporator. All of those components operate at different temperature levels. A solar collector with a tubular adsorbent configuration is proposed and numerically investigated. In this study, a nonlinear auto-regressive model with exogenous input is applied for the prediction of adsorbent bed temperature during the heating and desorption period. The developed neuronal model uses the MATLAB Network toolbox to obtain a better configuration network, applying multilayer feed-forward, the TANSIG transfer function, and the back-propagation learning algorithm. The input parameters are ambient temperature and the uncontrolled natural factor of solar radiation. The output network contains a variable representing the adsorbent bed temperature. The values obtained from the network model were compared with the experimental data, and the prediction performance of the network model was examined using various performance parameters. The mean square error (MSE) and the statistical coefficient of determination (R2) values are excellent numerical criteria for evaluating the performance of a prediction tool. A well-trained neural network model produces small MSE and higher R2 values. In the current study, the adsorbent bed temperature results obtained from a neural network with a two neuron in hidden layer and the number of the tapped time-delays d = 9 provided a reasonable degree of accuracy: MSE = 1.0121 and R2 = 0.99864 and the index of agreement was 0.9988. This network model, based on a high-performance algorithm, provided reliable and high-precision results concerning the predictable temperature of the adsorbent bed. [ABSTRACT FROM AUTHOR]

Motion control of modern and advanced aircraft has to be provided under conditions of incomplete and inaccurate knowledge of their parameters and characteristics, possible flight regimes, and environmental influences. In addition, a variety of abnormal situations may arise during flight, in particular, equipment failures and structural damage. The control system must be able to adapt to these changes by adjusting the control laws in use. The tools of the adaptive control allows us to meet this requirement. One of the effective approaches to the implementation of adaptivity concepts is the approach based on methods and tools of neural network modeling and control. In this case, a fairly common option in solving such problems is the use of recurrent neural networks, in particular, networks of NARX and NARMAX type. However, in a number of cases, in particular for control objects with complicated dynamic properties, this approach is ineffective. As a possible alternative, it is proposed to consider deep neural networks used both for modeling of dynamical systems and for their control. The capabilities of this approach are demonstrated on the example of a real applied problem, in which the control law of longitudinal angular motion of a supersonic passenger airplane is synthesized. The results obtained allow us to evaluate the effectiveness of the proposed approach, including the case of failure situations. [ABSTRACT FROM AUTHOR]

This article presents an innovative data-driven approach for examining long-term temporal rainfall patterns in the central highlands of West Papua, Indonesia. We utilized wavelet transforms to identify signs of a negative temporal correlation between the El Niño-Southern Oscillation (ENSO) and the 12-month Standardized Precipitation Index (SPI-12). Based on this cause-and-effect relationship, we employed dynamic causality modeling using the Nonlinear Autoregressive with Exogenous input (NARX) model to predict SPI-12. The Multivariate ENSO Index (MEI) was used as an attribute variable in this predictive framework. Consequently, this dynamic neural network model effectively captured common patterns within the SPI-12 time series. The implications of this study are significant for advancing data-driven precipitation models in regions characterized by intricate topography within the Indonesian Maritime Continent (IMC). [ABSTRACT FROM AUTHOR]