Nonlinear Autoregressive Exogenous Neural Network Research Articles

Food Demand Prediction Using the Nonlinear Autoregressive Exogenous Neural Network

Food demand prediction is a significant issue for both business process improvement and sustainable development. Data science methods, including artificial intelligence methods, are often used for this purpose. The aim of this research is to develop models for food demand prediction based on a nonlinear autoregressive exogenous neural network. The research focuses on processed foods, such as bread or butter. The architectures of the developed models differed in the number of hidden layers and the number of neurons in the hidden layers, as well as with different sizes of the delay line, were tested for a given product. The results of the research show that depending on the type of product, the prediction performance slightly differed. The results of the R2 measure ranged from 96,2399 to 99,6477, depending on particular products. The proposed models can be used in a company’s intelligent management system for the rational control of inventories and food production. This can also lead to a reduction in food waste.

A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of Timestamp Influence on Bitcoin Value

The transaction and market of bitcoin is volatile, meaning it’s uncertain because it changes frequently. There have been a number of research studies that have presented bitcoin price prediction models, but none of them have looked at the controlling variables linked with bitcoin transaction timestamps. It might be that price is not the only key criteria influencing bitcoin transactions, or the available model for bitcoin price prediction is yet to consider timestamp as a determining factor in its transaction. A better and more accurate model would be required to predict how the Timestamp influences changes of bitcoin transactions. That is why this current study utilized a Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the prediction of timestamp influence on Bitcoin value. Bitcoin historical datasets which are converted to a nonlinear regression into a “well-formulated” statistical problem in the manner of a ridge regression are used. Simulation analysis indicates that bitcoin digital currency’s performance variation is highly influenced by its transaction timestamp with the prediction accuracy of 96%. The contributions of this research lies with the fact that specific Bitcoin transaction events repeat themselves over and over again, meaning that the Open-Price, High-Price, Low-Price, and Close-Price of Bitcoin price over timestamp developed a pattern that was predicted by NARX with less That means those involved in the transaction of bitcoin at the wrong timestamp will certainly face the uncertainty negative effect of the bitcoin market.

Performance Comparison of Malaysian Air Pollution Index Prediction Using Nonlinear Autoregressive Exogenous Artificial Neural Network and Support Vector Machine

This paper compares the performance of Nonlinear Autoregressive Exogenous (NARX) Neural Network and Support Vector Machine (SVM) regression model to predict the Air Pollutant Index (API) in Malaysia. Two models namely the NARX and SVM regression were developed using the API and air quality time series data from three monitoring stations: Pasir Gudang, TTDI Jaya and Larkin. Hourly data of API and air quality parameters collected in year 2016 and 2018 were utilized to produce one step ahead API prediction. The air quality parameters consist of the NO2, SO2, CO, O3, PM2.5, PM10 concentration as well as three meteorological parameters which are wind speed, wind direction and ambient temperature. The NARX model was realized using a series-parallel feed-forward network. For the SVM regression model, different kernel functions: Linear, Quadratic, Cubic, Fine Gaussian, Medium Gaussian and Coarse Gaussian were evaluated. The performance of NARX and SVM regression was measured using the Root Mean Square Error (RMSE) and Coefficient of Determination (R2) values. Results show that the NARX model outperformed the SVM regression model in both 2016 and 2018 data respectively.

Permeate Flux Control in SMBR System by Using Neural Network Internal Model Control

This paper presents a design of a data-driven-based neural network internal model control for a submerged membrane bioreactor (SMBR) with hollow fiber for microfiltration. The experiment design is performed for measurement of physical parameters from an actuator input (permeate pump voltage), which gives the information (outputs) of permeate flux and trans-membrane pressure (TMP). The palm oil mill effluent is used as an influent preparation to depict fouling phenomenon in the membrane filtration process. From the experiment, membrane fouling potential is observed from flux decline pattern, with a rapid increment of TMP (above 200 mbar). Membrane fouling is a complex process and the available models in literature are not designed for control system (filtration performance). Therefore, this work proposes an aeration fouling control strategy to measure the filtration performance. The artificial neural networks (Feed-Forward Neural Network—FFNN, Radial Basis Function Neural Network—RBFNN and Nonlinear Autoregressive Exogenous Neural Network—NARXNN) are used to model dynamic behaviour of flux and TMP. In this case, only flux is used in closed loop control application, whereby the TMP effect is used for monitoring. The simulation results show that reliable prediction of membrane fouling potential is obtained. It can be observed that almost all the artificial neural network (ANN) models have similar shape with the actual data set, with the highest accuracy of more than 90% for both RBFNN and NARXN. The RBFNN is preferable due to simple structure of the network. In the control system, the RBFNN IMC depicts the highest closed loop performance with only 3.75 s (settling time) for setpoint changes when compared with other controllers. In addition, it showed fast performance in disturbance rejection with less overshoot. In conclusion, among the different neural network tested configurations the one based on radial basis function provides the best performance with respect to prediction as well as control performance.

Nexus between Energy Usability, Economic Indicators and Environmental Sustainability in Four ASEAN Countries: A Non-Linear Autoregressive Exogenous Neural Network Modelling Approach

This study investigates the use of a non-linear autoregressive exogenous neural network (NARX) model to investigate the nexus between energy usability, economic indicators, and carbon dioxide (CO2) emissions in four Association of South East Asian Nations (ASEAN), namely Malaysia, Thailand, Indonesia, and the Philippines. Optimized NARX model architectures of 5-29-1, 5-19-1, 5-17-1, 5-13-1 representing the input nodes, hidden neurons and the output units were obtained from the series of models configured. Based on the relationship between the input variables, CO2 emissions were predicted with a high correlation coefficient (R) > 0.9. and low mean square errors (MSE) of 3.92 × 10−21, 4.15 × 10−23, 2.02 × 10−19, 1.32 × 10−20 for Malaysia, Thailand, Indonesia, and the Philippines, respectively. Coal consumption has the highest level of influence on CO2 emissions in the four ASEAN countries based on the sensitivity analysis. These findings suggest that government policies in the four ASEAN countries should be more intensified on strategies to reduce CO2 emissions in relationship with the energy and economic indicators.

A data-driven approach to estimate battery cell temperature using a nonlinear autoregressive exogenous neural network model

Abstract Battery cell temperature is a key parameter in battery life degradation, safety, and dynamic performance. Intense charging-discharging operations and high-ambient temperatures escalate battery cell temperature, which in turn accelerates its degradation. Therefore, accurate battery cell temperature estimation can play a significant role in ensuring the optimal operation of a battery energy storage system (BESS). In order to estimate battery cell temperature as accurate as possible, use of non-linear models is imperative due to the non-linear nature of the battery operation. This paper proposes a data-driven model based on a Non-linear Autoregressive Exogenous (NARX) neural network to estimate battery cell temperatures in a utility-scale BESS, considering strongly-correlated independent variables, e.g., charging-discharging current and ambient temperature. Due to different temperature and weather characteristics in each season, seasonal NARX models have also been derived and compared with the universal one. The proposed models’ performance has been verified using the field data collected from a grid-connected BESS within a PV plant. The simulation results show high accuracy of the proposed model compared to the measured data for both seasonal and universal models without considering the complexity of the large-scale battery and container thermal dynamics. In particular, in more than 95% of the time, the estimated values yield root mean squared errors (RMSE) below 1 ∘C in different conditions, which confirms the validity and accuracy of the proposed model. Moreover, seasonal models show better performance with 18% to 50% less RMSE on average (for 1 h to 24 h forward estimation) compared to the universal model.

Hybrid nonlinear autoregressive neural network—Weibull statistical model applied to the supercritical extraction of lanolin from raw wool

Supercritical extraction of lanolin from raw wool with modified CO2 (5% ethanol) at temperatures above the melting point of lanolin (T = 36–42 oC) is difficult to model because of the multicomponent diffusion in the liquid layer. In this work, a neural network model is proposed based on the experiments previously published by our research group. Experimentally, the extraction of a 100-cm3 packed bed of raw wool depends on five variables, i.e., temperature (60–80 oC), pressure (120–200 bar), solvent mass flow rate (3–5 kg/h), wool packing density (127–318 kg/m3), and time (~ 1 h). A nonlinear autoregressive exogenous (5,3,1) neural network was designed and trained with the experimental data augmented using an empirical Weibull statistical function. This correctly predicts the lanolin breakthrough at the extractor exit with only $$\pm$$ 0.42% error. The simple arithmetics of neural network allows a fast optimization with Genetic Algorithm to find optimum operation conditions for the extraction process.

A Novel Neural Network-Based SINS/DVL Integrated Navigation Approach to Deal with DVL Malfunction for Underwater Vehicles

A navigation grade Strapdown Inertial Navigation System (SINS) combined with a Doppler Velocity Log (DVL) is widely used for autonomous navigation of underwater vehicles. Whether the DVL is able to provide continuous velocity measurements is of crucial importance to the integrated navigation precision. Considering that the DVL may fail during the missions, a novel neural network-based SINS/DVL integrated navigation approach is proposed. The nonlinear autoregressive exogenous (NARX) neural network, which is able to provide reliable predictions, is employed. While the DVL is available, the neural network is trained by the body frame velocity and its increment from the SINS and the DVL measurements. Once the DVL fails, the well trained network is able to forecast the velocity which can be used for the subsequent navigation. From the experimental results, it is clearly shown that the neural network is able to provide reliable velocity predictions for about 200 s–300 s during DVL malfunction and hence maintain the short-term accuracy of the integrated navigation.

Congestion Prediction System With Artificial Neural Networks

Software Defined Network (SDN) is a programmable network architecture that provides innovative solutions to the problems of the traditional networks. Congestion control is still an uncharted territory for this technology. In this work, a congestion prediction scheme has been developed by using neural networks. Minimum Redundancy Maximum Relevance (mRMR) feature selection algorithm was performed on the data collected from the OMNET++ simulation. The novelty of this study also covers the implementation of mRMR in an SDN congestion prediction problem. After evaluating the relevance scores, two highest ranking features were used. On the learning stage Nonlinear Autoregressive Exogenous Neural Network (NARX), Nonlinear Autoregressive Neural Network, and Nonlinear Feedforward Neural Network algorithms were executed. These algorithms had not been used before in SDNs according to the best of the authors knowledge. The experiments represented that NARX was the best prediction algorithm. This machine learning approach can be easily integrated to different topologies and application areas.

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.

Modelling and control of fouling in submerged membrane bioreactor using neural network internal model control

Membrane bioreactor (MBR) is one of the best solutions for water and wastewater treatment systems in producing high quality effluent that meets its standard regulations. However, fouling is one of the main issues in membrane filtration for membrane bioreactor system. The prediction of fouling is crucial in the membrane bioreactor control system design. This paper presents an intelligence modeling system so called artificial neural network (ANN). The feedforward neural network (FFNN), radial basis function neural network (RBFNN) and nonlinear autoregressive exogenous neural network (NARXNN) are applied to model the submerged MBR filtration system. The simulation results show good predictions for all methods which the highest performance of the model given by RBFNN. Based on the developed models, the neural network internal model control (NNIMC) is implemented to control fouling development in membrane filtration process. Three different control structures of the NNIMC are proposed. The FFNN IMC, RBFNN IMC and NARXNN IMC controllers are compared to the conventional IMC. The RBFNN IMC has a superior performance both in tracking and disturbance rejections.

Neural Observer for Nonlinear State and Input Estimation in a Truck-Semitrailer Combination

Driver assistance systems have become an indispensable part of today’s vehicles technology. Especially in the commercial vehicle sector, the challenges in obtaining information increase with rising system complexity. Compared to trucks, trailers for commercial vehicle combinations are sparsely equipped with electronic components. This leads to difficulties in implementation of intelligent systems for the trailer as necessary information is not provided. Reasons for this can be an insufficient sensor equipment due to uneconomical costs or a missing communication channel between the two vehicle units, preventing the transmission of required truck related information to the trailer. A possible model-based method to obtain unmeasured states is the Extended Kalman Filter. However, this approach requires elaborate preliminary work steps of high complexity and a sophisticated domain knowledge. Alternatively, this paper proposes the applicability of Neural Networks for estimating the required state and input variables, namely the articulation angle and the truck’s steering angle. Two different network types are used: the Feedforward Neural Network and the Nonlinear Autoregressive Exogenous Neural Network. The measured input variables for the networks, in accordance with the inputs of the Extended Kalman Filter in a previous publication, are merely trailer yaw rate and longitudinal speed. In conclusion, a comparison between the results of the Neural Networks and those of the Extended Kalman Filter is drawn.

Exploiting White Spaces for Karachi through Artificial Intelligence: Comparison of NARX and Cascade Feed Forward Back Propagation

Marriage of Internet of Everything (IoE) and Cognitive Radio driven technologies seems near under the umbrella of 6G and 6G+ communication standard. The expected new services that will be introduced in 6G communication will require high data rates for transmission. The learning based algorithms will play a key role towards successful implementation of these novel technologies and evolving next generation wireless standards for providing ubiquitous connectivity. This paper investigates performance of two artificial neural network (ANN) based algorithms for Karachi. These include Nonlinear autoregressive exogenous Algorithm (NARX) and cascade feed forward back propagation neural network (CFFBNN) scheme. A dataset for Karachi is also developed for 1805 MHZ. The results of the two algorithms are compared that show Mean Square Error (MSE) for CFFBNN is 6.8877e-5 at epoch 16 and MSE for NARX is 3.1506e-11 at epoch 26. Hence, exploiting computational performance, NARX performs much superior than the classis CFFBNN algorithm.

FORECASTING OIL PALM PRODUCTION BASED ON A NONLINEAR AUTOREGRESSIVE EXOGENOUS (NARX) NEURAL NETWORK MODEL

time series data analysis and prediction tool that is able to predict the yield of oil palm is needed to ensure an acceptable forecasting quality. An attempt was made in this study to develop a Nonlinear Autoregressive Exogenous (NARX) neural network model of oil palm production using MATLAB. This NARX model was used to predict the yield of oil palm in the states of Kelantan, Johor, Sabah and Sarawak in Malaysia. The performance of the NARX model was tested and validated using the Levenberg-Marquardt (LM) training algorithm and was compared with the Autoregressive Integrated Moving Average (ARIMA) model. The best performance of the NARX model was achieved at 70 per cent: 15 per cent: 15 per cent, with 10 neurons in the hidden layers and a delay value of four for Sarawak. For Kelantan and Johor, the NARX model produced the best result using the parameters of 70 per cent:10 per cent:20 per cent, with 13 neurons in the hidden layers and a delay value of four. The best result for Sabah was achieved using the parameters of 70 per cent: 15 per cent: 15 per cent, with 13 neurons in the hidden layers and a delay value of four. The results demonstrated that the proposed NARX model was more effective in modeling and forecasting time series data than the ARIMA model. The NARX model registered a minimum mean square error and mean absolute percentage error with a maximum average accuracy percentage and correlation coefficient. Keywords: Oil palm cultivation, yield predictions, nonlinear autoregressive exogenous neural network, autoregressive integrated moving average.

Cooling Load Forecasting via Predictive Optimization of a Nonlinear Autoregressive Exogenous (NARX) Neural Network Model

Accurate calculations and predictions of heating and cooling loads in buildings play an important role in the development and implementation of building energy management plans. This study aims to improve the forecasting accuracy of cooling load predictions using an optimized nonlinear autoregressive exogenous (NARX) neural network model. The preprocessing of training data and optimization of parameters were investigated for model optimization. In predictive models of cooling loads, the removal of missing values and the adjustment of structural parameters have been shown to help improve the predictive performance of a neural network model. In this study, preprocessing the training data eliminated missing values for times when the heating, ventilation, and air-conditioning system is not running. Also, the structural and learning parameters were adjusted to optimize the model parameters.

Weighed query-specific distance and hybrid NARX neural network for video object retrieval

Abstract The technical revolution in the field of video recording using the surveillance videos has increased the amount of the video databases that caused the need for an efficient video management system. This paper proposes a hybrid model using the nearest search algorithm (NSA) and the Levenberg–Marquardt (LM)-based non-linear autoregressive exogenous (NARX) neural network for performing the video object retrieval using the trajectories. Initially, the position of the objects in the video are retrieved using NSA and NARX individually, and they are averaged to determine the position of the object. The positions determined using the hybrid model is compared with the original database, and the trajectories of the objects are retrieved based on the minimum distance, which depends on the weighed query-specific distance. Experiments have been carried out using seven videos taken from the CAVIAR dataset, and the performance of the proposed method is compared with the existing methods. This proposed method found to be better than the existing method with respect to multiple object tracking precision (MOTP), multiple object tracking accuracy (MOTA), average tracking accuracy (ATA), precision, recall and F-measure that results a greater MOTP rate of 0.8796, precision rate of 0.8154, recall rate of 0.8408, the F-measure at a rate of 0.8371, MOTA of 0.8459 and ATA of 0.8324.

Multimodel ELM-Based Identification of an Aircraft Dynamics in the Entire Flight Envelope

The development of a multiple model-based identification algorithm is addressed in this paper for nonlinear modeling of a conventional aircraft in the entire flight envelope. The dynamic model of an aircraft varies significantly depending on changes in the flight condition of the air vehicle including the altitude and the equivalent air speed. Therefore, the conventional identification approaches for generating a single nonlinear model with time-invariant parameters cannot be used in the entire flight envelope of an aircraft. Accordingly, a multiple model-based approach using nonlinear autoregressive exogenous neural networks is introduced in this paper as a powerful tool in identifying complex nonlinear dynamic systems. Different methods of validity function determination are introduced in order to aggregate the separate local models into a single model. The obtained results show that the proposed approach using the extreme learning machine-based validity function determination method has a significant capability to predict the aircraft outputs in various flight conditions. Further, the proposed identification scheme can be used effectively as a precise multistep ahead predictor of nonlinear multivariable systems outputs.

Multivariate NARX neural network in prediction gaseous emissions within the influent chamber of wastewater treatment plants

Several potential problems associated with emissions such as odour nuisance may occur in sewer systems and wastewater treatment plants (WWTP). Improved understanding of prediction of emitted gases (e.g. hydrogen sulphide, H2S) is of great importance for better evaluation of such problems in WWTPs. The present paper provides a survey of the feasibility of utilising soft computing models in predicting emission factors (gaseous H2S) of based on five input parameters including the total dissolved sulphides, biochemical oxygen demand (BOD5), temperature, flow rate and pH. Multivariate nonlinear autoregressive exogenous (NARX) neural networks were developed and applied to predict weekly H2S in four WWTPS. Due to the nonlinearity nature of the H2S emission, the principle component analysis, and the average mutual information (AMI) method was employed to determine the effective independent parameters and the optimal time lags in state space for the H2S data. In addition to the NARX models, three standard regressive models (multiple linear regression model, stepwise regression and two-variate logarithmic regression model) were also applied to the same dataset for evaluating the capability and reliability of the developed NARX models. Based on several statistical measures (RMSE, MAPE, PCC, NSE & GRI), it was found that the NARX models were superior to the other applied models in predicting emitted H2S. Although the temperature and wastewater flow rate were found to be the most effective parameters for modelling H2S, but the results indeed showed that taking into account of all wastewater parameters would lead to more accurate H2S prediction.

Neural Network Approach to Forecast Hourly Intense Rainfall Using GNSS Precipitable Water Vapor and Meteorological Sensors

This work presents a methodology for the short-term forecast of intense rainfall based on a neural network and the integration of Global Navigation and Positioning System (GNSS) and meteorological data. Precipitable water vapor (PWV) derived from GNSS is combined with surface pressure, surface temperature and relative humidity obtained continuously from a ground-based meteorological station. Five years of GNSS data from one station in Lisbon, Portugal, are processed. Data for precipitation forecast are also collected from the meteorological station. Spaceborne Spinning Enhanced Visible and Infrared Imager (SEVIRI) data of cloud top measurements are also gathered, providing collocated information on an hourly basis. In previous studies it was found that the time-varying PWV is correlated with rainfall and can be used to detected heavy rain. However, a significant number of false positives were found, meaning that the evolution of PWV does not contain enough information to infer future rain. In this work, a nonlinear autoregressive exogenous neural network model (NARX) is used to process the GNSS and meteorological data to forecast the hourly precipitation. The proposed methodology improves the detection of intense rainfall events and reduces the number of false positives, with a good classification score varying from 63% up to 72% and a false positive rate of 36% down to 21%, for the tested years in the dataset. A score of 64% for intense rain events classification with 22% false positive rate is obtained for the most recent years. The method also achieves an almost 100% hit rate for the rain vs no rain detection, with close to no false alarms.