Nonlinear Autoregressive Exogenous Network Research Articles

Short-term wind speed forecasting over complex terrain using linear regression models and multivariable LSTM and NARX networks in the Andes Mountains, Ecuador

Wind speed forecasting systems over complex terrain at high altitude are very complex and conventional forecasting systems are unable to be applied due to wind variability. This study proposes an approach developed specifically for this study with application of linear regression models as baseline, and Recurrent Neural Networks (RNN): Long Short Term Memory (LSTM) network, and Dynamic Neural Networks (DNN): Nonlinear Autoregressive Exogenous (NARX) network to perform accurate wind speed forecasting in complex terrain in the Ecuadorian Andes to identify feasible places for wind energy applications. This work starts with the installation of two meteorological stations within of the mountainous zone of study to collect measured variables during 2018. Later on, the measured variables were evaluated by using statistical tools and Pearson Correlation Coefficient (PCC) to determine the input variables. Finally, the proposed forecasting models were trained, validated, and tested by using measured data. The DNN and RNN models were compared to determine the best performance through the statistical error forecast measurements as follows, Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and correlation coefficient. The comparison results of the proposed models indicated that the most precise values were for multivariable LSTM network, suggesting this model as a powerful approach to forecast wind speed over complex terrain, demonstrating the importance by using measured variables. Furthermore, the forecasted wind speed showed high values which are suitable for high wind power generation.

Recognition of Emotion in Indian Classical Dance Using EMG Signal

Indian classical dance forms like Kathak are an enrichment of Indian culture and tradition. These dance forms glorify its beauty by expressing nine emotions (Navras) such as Adbhut (amazed), Bhayanaka (fearful), Hasya (humorous), Karuna (tragic), Raudra (fierce), Shringar (loving smile), Veer (heroic), Bibhatsa (disgusted), and Shant (peaceful). Identifying correct emotions is an important task. The objective of this research work is to recognize Navras in the Kathak dance. Proposed research work can assist dance teachers in an accurate and unbiased evaluation process of dance examination. This research work analyzed the Electromyogram (EMG) signals acquired from eleven subjects. The EMG signals collected from the various locations on the face and neck represent the emotions and head movement. The EMG signals are processed to extract integrated EMG (IEMG) features. This research introduced a new feature named 'difference in IEMG feature' for improving the accuracy of emotion recognition. For the classification of nine emotions, the Least Square Support Vector Machine (LSSVM), Nonlinear Autoregressive Exogenous Network (NARX), and Long- and Short-Term Memory (LSTM) classifiers were used. The classifiers' performance is judged with head motion and without head motion. The classification accuracies are calculated using a maximum, variance, and mean of the feature. LSSVM, NARX, and LSTM classifiers achieved 60.80%, 81.67%, and 92.28% classification accuracies, respectively, using the IEMG feature and head motion. Using the new feature, LSSVM, NARX, and LSTM classifiers achieved 64.29%, 81.27%, and 93.63% classification accuracies, respectively. The overall classification accuracy improved by 1.46% by using the new feature.

Prediction of spring flows using nonlinear autoregressive exogenous (NARX) neural network models.

In the Mediterranean area, climate changes have led to long and frequent droughts with a drop in groundwater resources. An accurate prediction of the spring discharge is an essential task for the proper management of the groundwater resources and for the sustainable development of large areas of the Mediterranean basin. This study shows an unprecedented application of non-linear AutoRegressive with eXogenous inputs (NARX) neural networks to the prediction of spring flows. In particular, discharge prediction models were developed for 9 monitored springs located in the Umbria region, along the carbonate ridge of the Umbria-Marche Apennines. In the modeling, the precipitation was also considered as an exogenous input parameter. Good performances were achieved for all the springs and for both short-term and long-term predictions, passing from a lag time equal to 1month (R2 = 0.9012-0.9842, RAE = 0.0933-0.2557) to 12months (R2 = 0.9005-0.9838, RAE = 0.0963-0.2409). The forecasting sensitivity to changes in the temporal resolution, passing from weekly to monthly, was also assessed. The good results achieved recommend the use of the NARX network for spring discharge prediction in other areas characterized by karst aquifers.

Tide Prediction in the Venice Lagoon Using Nonlinear Autoregressive Exogenous (NARX) Neural Network

In the Venice Lagoon some of the highest tides in the Mediterranean occur, which have influenced the evolution of the city of Venice and the surrounding lagoon for centuries. The forecast of “high waters” in the lagoon has always been a matter of considerable practical interest. In this study, tide prediction models were developed for the entire lagoon based on Nonlinear Autoregressive Exogenous (NARX) neural networks. The NARX-based model development was performed in two different stages. The first stage was the training and testing of the NARX network, performed on data collected in a given time interval at the tide gauge of Punta della Salute, at the end of Canal Grande. The second stage consisted of a comprehensive validation of the model in the entire Venice Lagoon, with a detailed analysis of data from three measuring stations located in points of the lagoon with different characteristics. Good predictions were achieved regardless of whether the meteorological parameters were considered among input parameters, even with considerable time advance. Furthermore, the forecasting model based on NARX has proved capable of predicting even exceptional high tides. The proposed model could be a useful support tool for the management of the MOSE system, which will protect Venice from high waters.

A Time Series Prediction Model of Foundation Pit Deformation Based on Empirical Wavelet Transform and NARX Network

Large deep foundation pits are usually in a complex environment, so their surface deformation tends to show a stable rising trend with a small range of fluctuation, which brings certain difficulty to the prediction work. Therefore, in this study we proposed a nonlinear autoregressive exogenous (NARX) prediction method based on empirical wavelet transform (EWT) pretreatment is proposed for this feature. Firstly, EWT is used to conduct adaptive decomposition of the measured deformation data and extract the modal signal components with characteristic differences. Secondly, the main components affecting the deformation of the foundation pit are analyzed as a part of the external input. Then, we established a NARX prediction model for different components. Finally, all predicted values are superpositioned to obtain a total value, and the result is compared with the predicted results of the nonlinear autoregressive (NAR) model, empirical mode decomposition-nonlinear autoregressive (EMD-NAR) model, EWT-NAR model, NARX model, EMD-NARX model and EWT-NARX model. The results showed that, compared with the EWT-NAR and EWT-NARX models, the EWT-NARX model reduced the mean square error of KD25 by 91.35%, indicating that the feature of introducing external input makes NARX more suitable for combining with the EWT method. Meanwhile, compared with the EMD-NAR and EWT-NAR models, the introduction of the NARX model reduced the mean square error of KD25 by 78.58% and 95.71%, indicating that EWT had better modal decomposition capability than EMD.

Comparison of static MLP and dynamic NARX neural networks for forecasting of atmospheric PM10 and SO2 concentrations in an industrial site of Turkey

This study aims to compare performances of two static and one dynamic neural networks used for prediction of hourly ambient air quality concentrations in an industrial site of Turkey. Two air pollutants (PM10 and SO2) and three meteorological parameters (ambient air temperature, relative humidity, and wind speed) were used as input variables. The predictions of the dynamic nonlinear autoregressive exogenous (NARX) model were compared with the predictions of the static multilayer perceptron (MLP) neural network model. The results showed that the predictions of the NARX neural network were obviously better than the predictions of MLP networks. The coefficient of determination (R2), index of agreement and efficiency between the observed and predicted air pollutant concentrations by the NARX model were 0.9773, 0.994, and 0.977 for PM10, respectively while the same parameters were 0.9984, ≈1, and ≈1 for SO2. The MBEs (mean bias errors) were also approximately zero for both pollutants that indicate the adequacy of the model. The values of RMSE (root mean squared error) were also fractional as 0.0191 and 0.0087 for both pollutants. The NARX model predicted SO2 concentrations better than PM10 concentrations. In comparison with MLP network structures, NARX network exhibits faster convergence. The model suggested in this study could be used to support and improve air quality management practices.

Intelligent PV Panels Fault Diagnosis Method Based on NARX Network and Linguistic Fuzzy Rule-Based Systems

The expanding use of photovoltaic (PV) systems as an alternative green source for electricity presents many challenges, one of which is the timely diagnosis of faults to maintain the quality and high productivity of such systems. In recent years, various studies have been conducted on the fault diagnosis of PV systems. However, very few instances of fault diagnostic techniques could be implemented on integrated circuits, and these techniques require costly and complex hardware. This work presents a novel and effective, yet small and implementable, fault diagnosis algorithm based on an artificial intelligent nonlinear autoregressive exogenous (NARX) neural network and Sugeno fuzzy inference. The algorithm uses Sugeno fuzzy inference to isolate and classify faults that may occur in a PV system. The fuzzy inference requires the actual sensed PV system output power, the predicted PV system output power, and the sensed surrounding conditions. An artificial intelligent NARX-based neural network is used to obtain the predicted PV system output power. The actual output power of the PV system and the surrounding conditions are obtained in real-time using sensors. The algorithm is proven to be implementable on a low-cost microcontroller. The obtained results indicate that the fault diagnosis algorithm can detect multiple faults such as open and short circuit degradation, faulty maximum power point tracking (MPPT), and conditions of partial shading (PS) that may affect the PV system. Moreover, radiation and temperature, among other non-linear associations of patterns between predictors, can be captured by the proposed algorithm to determine the accurate point of the maximum power for the PV system.

Comparison of multiple regression analysis using dummy variables and a NARX network model: an example of a heavy metal adsorption process

AbstractIn the present study, the adsorption characteristics of coal fly ash obtained from the Kangal Power Plant, Turkey and activated fly ash in the planetary ball mill were investigated to remove the heavy metal ions from aqueous solutions. The adsorption capacity was compared for the first time using a multiple regression analysis with dummy variables and a non‐linear auto regressive exogenous (NARX) network model. An equation was obtained for all types of adsorbents or heavy metals using the regression of qe on the dummy variables. The predictive ability of NARX was found to be better than that of multiple regression using dummy variables. These models can also be successfully implemented on the experimental data to evaluate the adsorption process. In addition, fly ash is a low cost alternative since it is a more economical and environmentally friendly adsorbent and it is abundant in both nature and from waste material from industry.

Performance comparison of artificial neural network models for daily rainfall prediction

With an aim to predict rainfall one-day in advance, this paper adopted different neural network models such as feed forward back propagation neural network (BPN), cascade-forward back propagation neural network (CBPN), distributed time delay neural network (DTDNN) and nonlinear autoregressive exogenous network (NARX), and compared their forecasting capabilities. The study deals with two data sets, one containing daily rainfall, temperature and humidity data of Nilgiris and the other containing only daily rainfall data from 14 rain gauge stations located in and around Coonoor (a taluk of Nilgiris). Based on the performance analysis, NARX network outperformed all the other networks. Though there is no major difference in the performances of BPN, CBPN and DTDNN, yet BPN performed considerably well confirming its prediction capabilities. Levenberg Marquardt proved to be the most effective weight updating technique when compared to different gradient descent approaches. Sensitivity analysis was instrumental in identifying the key predictors.

PREDICTION OF ABOVE-ELBOW MOTIONS IN AMPUTEES, BASED ON ELECTROMYOGRAPHIC(EMG) SIGNALS, USING NONLINEAR AUTOREGRESSIVE EXOGENOUS (NARX) MODEL

Introduction In order to improve the quality of life of amputees, biomechatronic researchers and biomedical engineers have been trying to use a combination of various techniques to provide suitable rehabilitation systems. Diverse biomedical signals, acquired from a specialized organ or cell system, e.g., the nervous system, are the driving force for the whole system. Electromyography(EMG), as an experimental technique,is concerned with the development, recording, and analysis of myoelectric signals. EMG-based research is making progress in the development of simple, robust, user-friendly, and efficient interface devices for the amputees. Materials and Methods Prediction of muscular activity and motion patterns is a common, practical problem in prosthetic organs. Recurrent neural network (RNN) models are not only applicable for the prediction of time series, but are also commonly used for the control of dynamical systems. The prediction can be assimilated to identification of a dynamic process. An architectural approach of RNN with embedded memory is Nonlinear Autoregressive Exogenous (NARX) model, which seems to be suitable for dynamic system applications. Results Performance of NARX model is verified for several chaotic time series, which are applied as input for the neural network. The results showed that NARX has the potential to capture the model of nonlinear dynamic systems. The R-value and MSE are and , respectively. Conclusion EMG signals of deltoid and pectoralis major muscles are the inputs of the NARX network. It is possible to obtain EMG signals of muscles in other arm motions to predict the lost functions of the absent arm in above-elbow amputees, using NARX model.

Comparison of Neural Network Models in the Estimation of the Performance of Solar Still Under Jordanian Climate

Abstrac t—Three Artificial Neural Network models (Feedforward, Elman, and Nonlinear Autoregressive Exogenous (NARX) networks) were used to find the performance of triple solar still operating under Jordanian climate. Previously obtained experimental data was used to train the neural network. time, Hourly variation of cover glass temperature (Tg), w ater temperature in the upper basin (Tw1), w ater temperature in the middle basin (Tw2) and w ater temperature in the lower basin of the triple basin still (Tw3), Distillate volume, ambient temperature (Ta), plate temperature (TP) and hour ly solar intensity (Is) w ere used in the input layer of the network. The thermal efficiency (η) of a triple basin solar still was in the output layer. The obtained results were verified against previously obtained experimental data. It was found that Artificial Neural Network technique may be used to estimate the efficiency of the triple solar still with excellent accuracy with the coefficient of determination of Elman, feedforward and NARX models were found to be 0.9036, 0.99838 and 0.99863, respectively. The obtained results showed that feedforward model had the best ability to estimate the required performance, while NARX and Elman network had the lowest ability to estimate it.

Development of Neural Networks for Enhancement of Thermal Energy Storage using Phase Change Material

Three Artificial Neural Network models (Feedforward, Elman, and Nonlinear Autoregressive Exogenous (NARX) networks) were used to find the performance of a thermal energy storage system with and without a phase change material. Previously obtained experimental data was used to train the neural network. Time, mass of water, mass flow rate, number of balls containing the PCM, hourly solar radiation, ambient temperature and inlet water temperature were used in the input layer of the network. The outlet water temperature was in the output layer. The obtained results were verified against previously obtained experimental data. It was found that Artificial Neural Network technique could be used to estimate the outlet temperature with excellent accuracy with the coefficient of determination of Elman, feedforward and NARX models were found to be 0.95006, 0.99411 and 0.88185, respectively. The obtained results showed that feedforward model had the best ability to estimate the required performance, while NARX and Elman network had the lowest ability to estimate it.