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Artykuły w czasopismach na temat „NARX Neural Network”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Zafri Wan Yahaya, Wan Muhammad, Fadhlan Hafizhelmi Kamaru Zaman, and Mohd Fuad Abdul Latip. "Prediction of energy consumption using recurrent neural networks (RNN) and nonlinear autoregressive neural network with external input (NARX)." Indonesian Journal of Electrical Engineering and Computer Science 17, no. 3 (2020): 1215. http://dx.doi.org/10.11591/ijeecs.v17.i3.pp1215-1223.

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Recurrent Neural Networks (RNN) and Nonlinear Autoregressive Neural Network with External Input (NARX) are recently applied in predicting energy consumption. Energy consumption prediction for depth analysis of how electrical energy consumption is managed on Tower 2 Engineering Building is critical in order to reduce the energy usage and the operational cost. Prediction of energy consumption in this building will bring great benefits to the Faculty of Electrical Engineering UiTM Shah Alam. In this work, we present the comparative study on the performance of prediction of energy consumption in T

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Wan, Muhammad Zafri Wan Yahaya, Hafizhelmi Kamaru Zaman Fadhlan, and Fuad Abdul Latip Mohd. "Prediction of energy consumption using recurrent neural networks (RNN) and nonlinear autoregressive neural network with external input (NARX)." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 17, no. 3 (2020): 1215–23. https://doi.org/10.11591/ijeecs.v17.i3.pp1215-1223.

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Recurrent Neural Networks (RNN) and Nonlinear Autoregressive Neural Network with External Input (NARX) are recently applied in predicting energy consumption. Energy consumption prediction for depth analysis of how electrical energy consumption is managed on Tower 2 Engineering Building is critical in order to reduce the energy usage and the operational cost. Prediction of energy consumption in this building will bring great benefits to the Faculty of Electrical Engineering UiTM Shah Alam. In this work, we present the comparative study on the performance of prediction of energy consumption in T

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Shen, H. Y., and L. C. Chang. "Online multistep-ahead inundation depth forecasts by recurrent NARX networks." Hydrology and Earth System Sciences 17, no. 3 (2013): 935–45. http://dx.doi.org/10.5194/hess-17-935-2013.

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Abstract. Various types of artificial neural networks (ANNs) have been successfully applied in hydrological fields, but relatively scant on multistep-ahead flood inundation forecasting, which is very difficult to achieve, especially when dealing with forecasts without regular observed data. This study proposes a recurrent configuration of nonlinear autoregressive with exogenous inputs (NARX) network, called R-NARX, to forecast multistep-ahead inundation depths in an inundation area. The proposed R-NARX is constructed based on the recurrent neural network (RNN), which is commonly used for model

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Shen, H. Y., and L. C. Chang. "On-line multistep-ahead inundation depth forecasts by recurrent NARX networks." Hydrology and Earth System Sciences Discussions 9, no. 10 (2012): 11999–2028. http://dx.doi.org/10.5194/hessd-9-11999-2012.

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Abstract. Various types of artificial neural networks (ANNs) have been successfully applied in hydrological fields, but relatively scant on flood inundation forecast. This study proposes a recurrent configuration of nonlinear autoregressive with exogenous inputs (NARX) network, called R-NARX, to forecast multistep-ahead inundation depths in an inundation area. The proposed R-NARX is constructed based on the recurrent neural network (RNN), which is commonly used for modeling nonlinear dynamical systems. The models were trained and tested based on a large number of inundation data generated by a

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Xu, Yanxin, Dongjian Zheng, Chenfei Shao, Sen Zheng, Hao Gu, and Huixiang Chen. "Real-Time Diagnosis of Structural Damage Based on NARX Neural Network with Dynamic Response." Mathematics 11, no. 6 (2023): 1281. http://dx.doi.org/10.3390/math11061281.

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In order to improve the applicability of the time series model for structural damage diagnosis, this article proposed a real-time structural damage diagnosis method based on structural dynamic response and a recurrent neural network model. Starting from the transfer rate function of linear structure dynamic response, a generalized Auto-Regressive model with eXtra inputs (ARX) expression for a dynamic response under smooth excitation conditions was derived and extended to the case of nonlinear structure damage using a neural nonlinear ARX (NARX) network model. The method of NARX neural network

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Dhafer, Ali H., Fauzias Mat Nor, Gamal Alkawsi, et al. "Empirical Analysis for Stock Price Prediction Using NARX Model with Exogenous Technical Indicators." Computational Intelligence and Neuroscience 2022 (March 25, 2022): 1–13. http://dx.doi.org/10.1155/2022/9208640.

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Stock price prediction is one of the major challenges for investors who participate in the stock markets. Therefore, different methods have been explored by practitioners and academicians to predict stock price movement. Artificial intelligence models are one of the methods that attracted many researchers in the field of financial prediction in the stock market. This study investigates the prediction of the daily stock prices for Commerce International Merchant Bankers (CIMB) using technical indicators in a NARX neural network model. The methodology employs comprehensive parameter trails for d

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Natsheh and Samara. "Toward Better PV Panel’s Output Power Prediction; a Module Based on Nonlinear Autoregressive Neural Network with Exogenous Inputs." Applied Sciences 9, no. 18 (2019): 3670. http://dx.doi.org/10.3390/app9183670.

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Much work has been carried out for modeling the output power of photovoltaic panels. Using artificial neural networks (ANNS), one could efficiently model the output power of heterogeneous photovoltaic (HPV) panels. However, due to the existing different types of artificial neural network implementations, it has become hard to choose the best approach to use for a specific application. This raises the need for studies that develop models using the different neural networks types and compare the efficiency of these different types for that specific application. In this work, two neural network t

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Alsarayreh, Mohammad, Omar Mohamed, and Mustafa Matar. "Modeling a Practical Dual-Fuel Gas Turbine Power Generation System Using Dynamic Neural Network and Deep Learning." Sustainability 14, no. 2 (2022): 870. http://dx.doi.org/10.3390/su14020870.

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Accurate simulations of gas turbines’ dynamic performance are essential for improvements in their practical performance and advancements in sustainable energy production. This paper presents models with extremely accurate simulations for a real dual-fuel gas turbine using two state-of-the-art techniques of neural networks: the dynamic neural network and deep neural network. The dynamic neural network has been realized via a nonlinear autoregressive network with exogenous inputs (NARX) artificial neural network (ANN), and the deep neural network has been based on a convolutional neural network

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Zeng, Chao, Xiao Liu, Liyue Chen, Xianzhi He, and Zeyu Kang. "Enhanced Short-Term Temperature Prediction of Seasonally Frozen Soil Subgrades Using the NARX Neural Network." Applied Sciences 14, no. 22 (2024): 10257. http://dx.doi.org/10.3390/app142210257.

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Accurate prediction of subgrade temperatures in seasonally frozen regions is crucial for understanding thermal states, frost heave phenomena, stability, and other critical characteristics. This study employs a nonlinear autoregressive with exogenous input (NARX) network to predict short-term subgrade temperatures in the Golmud-Nagqu section of China’s National Highway 109. The methodology involves preprocessing subgrade monitoring data, including temperature, water content, and frost heave, followed by developing a temperature prediction model. This tailored NARX neural network, compared to th

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Liu, Quanliang, Ya Wang, and Mingwei Xu. "Fishing Vessel Trawl Winch Tension Control: A BP Neural Network PID Feedforward Control Method Based on NARX Neural Network Prediction." Processes 13, no. 7 (2025): 2001. https://doi.org/10.3390/pr13072001.

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In order to solve the problems of the poor adaptability to nonlinear systems, cumbersome parameter adjustment, and sensing-execution delay facing PID control for trawl winch tension control on fishing vessels, a prediction model for trawl winch cable tension was developed using a NARX neural network. The network was trained using historical data to achieve the accurate prediction of the trawl winch cable tension value in the future moment. The predicted value of the NARX neural network was introduced into the BP-PID controller as a feedforward quantity, and a BP-PID feedforward control strateg

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Chen, Changxu, and Zhenhai Pan. "A Neural Network-Based Method for Real-Time Inversion of Nonlinear Heat Transfer Problems." Energies 16, no. 23 (2023): 7819. http://dx.doi.org/10.3390/en16237819.

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Inverse heat transfer problems are important in numerous scientific research and engineering applications. This paper proposes a network-based method utilizing the nonlinear autoregressive with exogenous inputs (NARX) neural network, which can achieve real-time identification of thermal boundary conditions for nonlinear transient heat transfer processes. With the introduction of the NARX neural network, the proposed method offers two key advantages: (1) The proposed method can obtain inversion results using only surface temperature time series. (2) The heat flux can be estimated even when the

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Yang, Di. "Short-Term Load Monitoring of a Power System Based on Neural Network." International Transactions on Electrical Energy Systems 2023 (April 6, 2023): 1–10. http://dx.doi.org/10.1155/2023/4581408.

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In order to improve the accuracy of power load forecasting, this paper proposes a neural network-based short-term monitoring method. First, the original energy load signal is decomposed by the CEEMDAN algorithm to obtain several eigenmode function components and residual components; several eigenmode function components and residual functions are fed into the NARX neural network for computational purposes. The partial hypothesis is superimposed in the following part to obtain the final short-term forecast. According to the test results, the MAPE of the CEEMDAN-NARX model is 4.753%, 3.540%, and

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Braide, S. "Neural Network Technique in the Study of Selected Chemical Engineering Unit Operations Data using MATLAB." International Journal of Innovative Science and Research Technology 7, no. 5 (2022): 1717–23. https://doi.org/10.5281/zenodo.6827659.

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The concept of Artificial Neural networks was of McClloch and Pitts in 1943 and since then it has been studied in details by scientists and engineers alike. This is a study of the use of artificial neural network in analysis of selected chemical engineering unit operations. In this paper several networks were developed and trained for three different unit operations. This paper deals with the training of neural networks to perform predictions of several chemical unit operations. The feedforward neural network was trained to model the bubble point temperature and pressure of the water ethanol-w

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Younes, Boujoudar, Elmoussaoui Hassan, and Lamhamdi Tijani. "Lithium-ion batteries modeling and state of charge estimation using artificial neural network." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (2019): 3415–22. https://doi.org/10.11591/ijece.v9i5.pp3415-3422.

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In this paper, we propose an effective and online technique for modeling of Li-ion battery and estimation of State of Charge (SoC). Based on Feed Forward Neural Networks (FFNN) and Nonlinear Auto Regressive model with eXogenous input (NARX). The both Artificial Neural Network (ANN) are trained offline using the data collected from the experimental data. The NARX network finds the require battery votage in the FFNN network to estimate SoC. The proposed method is implemented on a Li-Ion battery cell and the results of simulation show a good accuracy and fast convergence of the proposed method.

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Gumus, Fatma, and Derya Yiltas-Kaplan. "Congestion Prediction System With Artificial Neural Networks." International Journal of Interdisciplinary Telecommunications and Networking 12, no. 3 (2020): 28–43. http://dx.doi.org/10.4018/ijitn.2020070103.

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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 hi

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Kacimi, Houda, Sara Fennane, Hamza Mabchour, Fatehi ALtalqi, and Adil Echchelh. "Evaluation of Nonlinear Autoregressive Network with Exogenous Inputs Architectures for Wind Speed forecasting." EPJ Web of Conferences 326 (2025): 05003. https://doi.org/10.1051/epjconf/202532605003.

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This research investigates the optimal NARX neural network architecture for forecasting daily maximum wind speed in Dakhla, a region with substantial wind energy resources. Two configurations NARX-SP (open loop) and NARX-P (closed loop) were evaluated using the Levenberg-Marquardt algorithm, known for its fast and efficient training. Predictive performance was assessed using RMSE to measure the gap between predicted and actual values. Results show that NARX-SP outperforms NARX-P, achieving lower RMSE and better forecasting accuracy.

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Bucci, Andrea. "Realized Volatility Forecasting with Neural Networks." Journal of Financial Econometrics 18, no. 3 (2020): 502–31. http://dx.doi.org/10.1093/jjfinec/nbaa008.

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Abstract In the last few decades, a broad strand of literature in finance has implemented artificial neural networks as a forecasting method. The major advantage of this approach is the possibility to approximate any linear and nonlinear behaviors without knowing the structure of the data generating process. This makes it suitable for forecasting time series which exhibit long-memory and nonlinear dependencies, like conditional volatility. In this article, the predictive performance of feed-forward and recurrent neural networks (RNNs) was compared, particularly focusing on the recently develop

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Irawan, Ruly, Mohd Shahir Liew, Montasir Osman Ahmed Ali, and Ahmad Mohamad Al Yacouby. "Prediction of dynamic responses of floating structures using NARX with mirroring technique." MATEC Web of Conferences 203 (2018): 01025. http://dx.doi.org/10.1051/matecconf/201820301025.

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Displacements, velocities and accelerations of Six Degree of freedom of a single floating structure was predicted using Time Series NARX feedback neural Networks. The nonlinear autoregressive network with exogenous inputs (NARX) is a recurrent dynamic network, with feedback connections enclosing several layers of the network is based on the linear ARX model, which is commonly used in time-series modelling is used in this study. Time series data of displacements of a single floating structure was used for training and testing the ANN model. In the training stage, this time series data of enviro

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Ali, Wasiq, Wasim Ullah Khan, Muhammad Asif Zahoor Raja, Yigang He, and Yaan Li. "Design of Nonlinear Autoregressive Exogenous Model Based Intelligence Computing for Efficient State Estimation of Underwater Passive Target." Entropy 23, no. 5 (2021): 550. http://dx.doi.org/10.3390/e23050550.

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In this study, an intelligent computing paradigm built on a nonlinear autoregressive exogenous (NARX) feedback neural network model with the strength of deep learning is presented for accurate state estimation of an underwater passive target. In underwater scenarios, real-time motion parameters of passive objects are usually extracted with nonlinear filtering techniques. In filtering algorithms, nonlinear passive measurements are associated with linear kinetics of the target, governing by state space methodology. To improve tracking accuracy, effective feature estimation and minimizing positio

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Amellas, Yousra, Saif Serag, Fahd Loukdache, Abdelouahed Djebli, and Adil Echchelh. "New method for wind potential prediction using recurrent artificial neural networks." E3S Web of Conferences 319 (2021): 01111. http://dx.doi.org/10.1051/e3sconf/202131901111.

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The aim of the study is to find the right architecture of the NARX neural network, in order to perform the daily prediction of the maximum wind speed of Laayoune city. We relied on the Levenberg-Marquardt optimization algorithm. The RMSE error metric showed that NARX-SP outperforms NARX-P.

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Deng, Liang, Haidong Li, Youtong Wang, Changxu Chen, and Zhenhai Pan. "Inverse algorithm for boundary heat flux density based on the NARX neural network." Journal of Physics: Conference Series 2865, no. 1 (2024): 012029. http://dx.doi.org/10.1088/1742-6596/2865/1/012029.

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Abstract The inverse heat transfer problem is vital for scientific research and engineering applications. This paper introduces a method using the Nonlinear Autoregressive with Exogenous Inputs (NARX) neural network to identify heat boundary conditions in nonlinear transient heat transfer processes in real time. This method has two notable advantages: (1) It relies solely on surface temperature time series to obtain inversion results; (2) Even in the absence of knowledge regarding the system’s state equations, it can estimate heat flux density. The NARX neural network is trained by using Bayes

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Syed Asad, Hussain, Yuen Richard Kwok Kit, and Lee Eric Wai Ming. "Energy Modeling with Nonlinear-Autoregressive Exogenous Neural Network." E3S Web of Conferences 111 (2019): 03059. http://dx.doi.org/10.1051/e3sconf/201911103059.

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The model-based predictive control (MPC) is considered to be an effective tool for optimal control of building heating, ventilation, and air-conditioning (HVAC) systems. MPC need to update the operating set points of the local control loops that have a significant influence on the energy performance of the system. Performance of MPC relies on the accuracy of the system performance model. There are two commonly used modeling approach – conventional or analytical approach that is the way of process modeling for some time, but it tends to increase the online computational load as it requires a fu

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Sani, Muhammad Gaya, Norhaliza Abdul Wahab, Yahya M. Sam, Sharatul Izah Samsudin, and Irma Wani Jamaludin. "Comparison of NARX Neural Network and Classical Modelling Approaches." Applied Mechanics and Materials 554 (June 2014): 360–65. http://dx.doi.org/10.4028/www.scientific.net/amm.554.360.

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Classical optimization tools are effective when precise mechanistic models exist to support their design and implementation. However, most of the real-world processes are complex due to either nonlinearities or uncertainties (or both) and environmental variations, thus making realizing accurate mathematical models for such processes quite difficult or often impossible. Black box approach tends to present a better alternative in such situations. This paper presents a comparison of nonlinear autoregressive with eXogenous (NARX) neural network and traditional modelling techniques [autoregressive

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Wang, Geng, Xuemin Yao, Jianjun Cui, Yonggang Yan, Jun Dai, and Wu Zhao. "A novel piezoelectric hysteresis modeling method combining LSTM and NARX neural networks." Modern Physics Letters B 34, no. 28 (2020): 2050306. http://dx.doi.org/10.1142/s0217984920503066.

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In order to study the hysteresis nonlinear characteristics of piezoelectric actuators, a novel hybrid modeling method based on Long-Short-Term Memory (LSTM) and Nonlinear autoregressive with external input (NARX) neural networks is proposed. First, the input–output curve between the applied voltage and the produced angle of a piezoelectric tip/tilt mirror is measured. Second, two hysteresis models named LSTM and NARX neural networks were, respectively, established mathematically, and then were tested and verified experimentally. Third, a novel adaptive weighted hybrid hysteresis model which co

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Louzazni, Mohamed, Heba Mosalam, and Daniel Tudor Cotfas. "Forecasting of Photovoltaic Power by Means of Non-Linear Auto-Regressive Exogenous Artificial Neural Network and Time Series Analysis." Electronics 10, no. 16 (2021): 1953. http://dx.doi.org/10.3390/electronics10161953.

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In this research paper, a nonlinear autoregressive with exogenous input (NARX) model of the nonlinear system based on neural network and time series analysis is proposed to deal with the one-month forecast of the produced power from photovoltaic modules (PVM). The PVM is a monocrystalline cell with a rated production of 175 watts that is placed at Heliopolis University, Bilbéis city, Egypt. The NARX model is considered powerful enough to emulate the nonlinear dynamic state-space model. It is extensively performed to resolve a variety of problems and is mainly important in complex process contr

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LUO, HUAIEN, and SADASIVAN PUTHUSSERYPADY. "SPATIO-TEMPORAL MODELING AND ANALYSIS OF fMRI DATA USING NARX NEURAL NETWORK." International Journal of Neural Systems 16, no. 02 (2006): 139–49. http://dx.doi.org/10.1142/s0129065706000561.

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This paper presents spatio-temporal modeling and analysis methods to fMRI data. Based on the nonlinear autoregressive with exogenous inputs (NARX) model realized by the Bayesian radial basis function (RBF) neural networks, two methods (NARX-1 and NARX-2) are proposed to capture the unknown complex dynamics of the brain activities. Simulation results on both synthetic and real fMRI data clearly show that the proposed schemes outperform the conventional t-test method in detecting the activated regions of the brain.

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Pavon, Wilson, Jorge Chavez, Diego Guffanti, and Ama Baduba Asiedu-Asante. "Unmanned Aerial Vehicle Position Tracking Using Nonlinear Autoregressive Exogenous Networks Learned from Proportional-Derivative Model-Based Guidance." Mathematical and Computational Applications 30, no. 4 (2025): 78. https://doi.org/10.3390/mca30040078.

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The growing demand for agile and reliable Unmanned Aerial Vehicles (UAVs) has spurred the advancement of advanced control strategies capable of ensuring stability and precision under nonlinear and uncertain flight conditions. This work addresses the challenge of accurately tracking UAV position by proposing a neural-network-based approach designed to replicate the behavior of classical control systems. A complete nonlinear model of the quadcopter was derived and linearized around a hovering point to design a traditional proportional derivative (PD) controller, which served as a baseline for tr

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Ali, Wasiq, Yaan Li, Muhammad Asif Zahoor Raja, Wasim Ullah Khan, and Yigang He. "State Estimation of an Underwater Markov Chain Maneuvering Target Using Intelligent Computing." Entropy 23, no. 9 (2021): 1124. http://dx.doi.org/10.3390/e23091124.

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In this study, an application of deep learning-based neural computing is proposed for efficient real-time state estimation of the Markov chain underwater maneuvering object. The designed intelligent strategy is exploiting the strength of nonlinear autoregressive with an exogenous input (NARX) network model, which has the capability for estimating the dynamics of the systems that follow the discrete-time Markov chain. Nonlinear Bayesian filtering techniques are often applied for underwater maneuvering state estimation applications by following state-space methodology. The robustness and precisi

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Niranjana Murthy, H. S. "Comparison Between Non-Linear Autoregressive and Non-Linear Autoregressive with Exogeneous Inputs Models for Predicting Cardiac Ischemic Beats." Journal of Computational and Theoretical Nanoscience 17, no. 9 (2020): 3974–78. http://dx.doi.org/10.1166/jctn.2020.9001.

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The prediction accuracy and generalization ability of neural models for forecasting Myocardial Ischemic Beats depends on type and architecture of employed network model. This paper presents the comparison analysis of recurrent neural network (RNN) architectures with embedded memory, Non-linear Autoregressive (NAR) and Non-linear Autoregressive with Exogeneous inputs (NARX) models for forecasting Ischemic Beats in ECG. Numerous architectures of the NAR and NARX models are verified for prediction and the performances are evaluated in terms of MSE. The performances of NAR and NARX models are vali

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Sun, Qihao, Changcheng Yin, and Baohua Wang. "The application of neural networks driven by nonlinear model data in road roughness estimation." Measurement Science and Technology 36, no. 2 (2024): 026004. https://doi.org/10.1088/1361-6501/ad9855.

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Abstract 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

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Zheng, Yihong, Wanjuan Zhang, Jingjing Xie, and Qiao Liu. "A Water Consumption Forecasting Model by Using a Nonlinear Autoregressive Network with Exogenous Inputs Based on Rough Attributes." Water 14, no. 3 (2022): 329. http://dx.doi.org/10.3390/w14030329.

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Scientific prediction of water consumption is beneficial for the management of water resources. In practice, many factors affect water consumption, and the various impact mechanisms are complex and uncertain. Meanwhile, the water consumption time series has a nonlinear dynamic feature. Therefore, this paper proposes a nonlinear autoregressive model with an exogenous input (NARX) neural network model based on rough set (RS) theory. First, the RS theory was used to analyze the importance of each attribute in water consumption. Then, the main influencing factor was selected as the input of the NA

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Jalal, Nour Aldeen, Tamer Abdulbaki Alshirbaji, and Knut Möller. "Predicting Surgical Phases using CNN-NARX Neural Network." Current Directions in Biomedical Engineering 5, no. 1 (2019): 405–7. http://dx.doi.org/10.1515/cdbme-2019-0102.

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AbstractOnline recognition of surgical phases is essential to develop systems able to effectively conceive the workflow and provide relevant information to surgical staff during surgical procedures. These systems, known as context-aware system (CAS), are designed to assist surgeons, improve scheduling efficiency of operating rooms (ORs) and surgical team and promote a comprehensive perception and awareness of the OR. State-of-the-art studies for recognizing surgical phases have made use of data from different sources such as videos or binary usage signals from surgical tools. In this work, we

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Wotzka, Daria, Grażyna Suchacka, Paweł Frącz, Łukasz Mach, Marzena Stec, and Joachim Foltys. "Analysis of the Housing Market Dynamics Using NARX Neural Network." Anwendungen und Konzepte der Wirtschaftsinformatik, no. 19 (August 10, 2024): 7. http://dx.doi.org/10.26034/lu.akwi.2024.5947.

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This study employs a Nonlinear Autoregressive with eXogenous inputs (NARX) neural network to model the dynamics of the housing construction market in Poland, with a distinction made between segments of developers and individual investors. The dataset under analysis contains the 19-year data corresponding to the numbers of housing units approved for construction, under construction, and completed. The NARX model was calibrated thoroughly to suit unique characteristics of the data, with an emphasis put on the hidden layer size and delay parameters, to capture the estate market's nonlinear trends

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Xin, Liang, Yuchao Wang, and Huixuan Fu. "Omnidirectional Mobile Robot Dynamic Model Identification by NARX Neural Network and Stability Analysis Using the APLF Method." Symmetry 12, no. 9 (2020): 1430. http://dx.doi.org/10.3390/sym12091430.

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In this paper, the NARX neural network system is used to identify the complex dynamics model of omnidirectional mobile robot while rotating with moving, and analyze its stability. When the mobile robot model rotates and moves at the same time, the dynamic model of the mobile robot is complex and there is motion coupling. The change of the model in different states is a kind of symmetry. In order to solve the problem that there is a big difference between the mechanism modeling motion simulation and the actual data, the dynamic model identification of mobile robot in special state based on NARX

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Song, Ziwen, Feng Sun, Rongji Zhang, Yingcui Du, and Chenchen Li. "Prediction of Road Network Traffic State Using the NARX Neural Network." Journal of Advanced Transportation 2021 (November 28, 2021): 1–17. http://dx.doi.org/10.1155/2021/2564211.

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To provide reliable traffic information and more convenient visual feedback to traffic managers and travelers, we proposed a prediction model that combines a neural network and a Macroscopic Fundamental Diagram (MFD) for predicting the traffic state of regional road networks over long periods. The method is broadly divided into the following steps. To obtain the current traffic state of the road network, the traffic state efficiency index formula proposed in this paper is used to derive it, and the MFD of the current state is drawn by using the classification of the design speed and free flow

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Xu, Yan Li, Hong Xun Chen, Wang Guo, and Qiu Yu Zhu. "A Comparison of NARX and BP Neural Network in Short-Term Building Cooling Load Prediction." Applied Mechanics and Materials 513-517 (February 2014): 1545–48. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.1545.

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A comparison of nonlinear autoregression with exogenous inputs (NARX) neural network and back-propagation (BP) neural network in short-term prediction of building cooling load is presented in this dissertation. Both predictive models have been applied in a group of commercial buildings and analysis of prediction errors has been highlighted. Training and testing data for both prediction models have been generated from DeST (Designers Simulation Toolkits) with climate data of Shanghai. The simulation results indicate that NARX method can achieve better accuracy and generalization ability than tr

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Shao, Yuehong, Jun Zhao, Jinchao Xu, Aolin Fu, and Min Li. "Application of Rainfall-Runoff Simulation Based on the NARX Dynamic Neural Network Model." Water 14, no. 13 (2022): 2082. http://dx.doi.org/10.3390/w14132082.

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The research into rainfall-runoff plays a very important role in water resource management. However, runoff simulation is a challenging task due to its complex formation mechanism, time-varying characteristics and nonlinear hydrological dynamic process. In this study, a nonlinear autoregressive model with exogenous input (NARX) is used to simulate the runoff in the Linyi watershed located in the northeastern part of the Huaihe river basin. In order to better evaluate the performance of NARX, a distributed hydrological model, TOPX, is used to simulate the discharge as a reference, and runoff cl

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Secci, Daniele, Maria Giovanna Tanda, Marco D'Oria, and Valeria Todaro. "Artificial intelligence models to evaluate the impact of climate change on groundwater resources." Journal of Hydrology Volume 627, Part B (2023): 130359. https://doi.org/10.1016/j.jhydrol.2023.130359.

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This study develops three different artificial intelligence (AI) models in order to investigate the effects of climate change on groundwater resources using historical records of precipitation, temperature and groundwater levels together with regional climate projections. In particular, the Non-linear Autoregressive Neural Network (NARX), the Long-Short Term Memory Neural Network (LSTM) and the Convolutional Neural Network (CNN) were compared. Considering an aquifer located in northern Italy as a case study, the neural networks were trained to replicate observed groundwater levels by taking as

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Liu, Xiaowei, Minghu Ha, Xiaohui Lei, and Zhao Zhang. "A Novel GRA-NARX Model for Water Level Prediction of Pumping Stations." Water 14, no. 19 (2022): 2954. http://dx.doi.org/10.3390/w14192954.

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It is necessary but difficult to accurately predict the water levels in front of the pumping stations of an open-channel water transfer project because of the complex interactions among hydraulic structures. In this study, a novel GRA-NARX (gray relation analysis—nonlinear auto-regressive exogenous) model is proposed based on a gray relation analysis (GRA) and nonlinear auto-regressive exogenous (NARX) neural network for 2 h ahead for the prediction of water levels in front of pumping stations, in which an improved algorithm of the NARX neural network is used to obtain the optimal combination

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Boujoudar, Younes, Hassan Elmoussaoui, and Tijani Lamhamdi. "Lithium-Ion batteries modeling and state of charge estimation using Artificial Neural Network." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (2019): 3415. http://dx.doi.org/10.11591/ijece.v9i5.pp3415-3422.

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<span class="fontstyle0">In This paper, we propose an effective and online technique for modeling nd State of Charge (SoC) estimation of Lithium-Ion (Li-Ion) batteries using Feed Forward Neural Networks(FFNN) and Nonlinear Auto Regressive model with eXogenous input(NARX). The both Artificial Neural Network (ANN) are rained using the data collected from the batterycharging and discharging pro ess. The NARX network finds the needed battery model, where the input ariables are the battery terminal voltage, SoC at the previous sample, and the urrent, temperature at the present sample. The pro

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Zulkiflee, Nurul Najihah, Noor Zuraidin Mohd Safar, Hazalila Kamaludin, Muhamad Hanif Jofri, Noraziahtulhidayu Kamarudin, and Rasyidah. "Rainfall-Runoff Modeling Using Artificial Neural Network for Batu Pahat River Basin." JOIV : International Journal on Informatics Visualization 8, no. 2 (2024): 613. http://dx.doi.org/10.62527/joiv.8.2.2704.

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This research delves into the effectiveness of Artificial Neural Networks with Multilayer Perceptron (ANN-MLP) and Nonlinear AutoRegressive with eXogenous inputs (NARX) models in predicting short-term rainfall-runoff patterns in the Batu Pahat River Basin. This study aims to predict river water levels using historical rainfall and river level data for future intervals of 1, 3, and 6 hours. Data preprocessing techniques, including the management of missing values, identification of outliers, and reduction of noise, were applied to enhance the accuracy and dependability of the models. This study

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Schornobay-Lui, Elaine, Eduardo Carlos Alexandrina, Mônica Lopes Aguiar, Werner Siegfried Hanisch, Edinalda Moreira Corrêa, and Nivaldo Aparecido Corrêa. "Prediction of short and medium term PM10 concentration using artificial neural networks." Management of Environmental Quality: An International Journal 30, no. 2 (2019): 414–36. http://dx.doi.org/10.1108/meq-03-2018-0055.

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Purpose There has been a growing concern about air quality because in recent years, industrial and vehicle emissions have resulted in unsatisfactory human health conditions. There is an urgent need for the measurements and estimations of particulate pollutants levels, especially in urban areas. As a contribution to this issue, the purpose of this paper is to use data from measured concentrations of particulate matter and meteorological conditions for the predictions of PM10. Design/methodology/approach The procedure included daily data collection of current PM10 concentrations for the city of

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Wong, C. X., and K. Worden. "Generalised NARX shunting neural network modelling of friction." Mechanical Systems and Signal Processing 21, no. 1 (2007): 553–72. http://dx.doi.org/10.1016/j.ymssp.2005.08.029.

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Tian, Ye, Yue-Ping Xu, Zongliang Yang, Guoqing Wang, and Qian Zhu. "Integration of a Parsimonious Hydrological Model with Recurrent Neural Networks for Improved Streamflow Forecasting." Water 10, no. 11 (2018): 1655. http://dx.doi.org/10.3390/w10111655.

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This study applied a GR4J model in the Xiangjiang and Qujiang River basins for rainfall-runoff simulation. Four recurrent neural networks (RNNs)—the Elman recurrent neural network (ERNN), echo state network (ESN), nonlinear autoregressive exogenous inputs neural network (NARX), and long short-term memory (LSTM) network—were applied in predicting discharges. The performances of models were compared and assessed, and the best two RNNs were selected and integrated with the lumped hydrological model GR4J to forecast the discharges; meanwhile, uncertainties of the simulated discharges were estimate

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Di Nunno, Fabio, Giovanni de Marinis, Rudy Gargano, and Francesco Granata. "Tide Prediction in the Venice Lagoon Using Nonlinear Autoregressive Exogenous (NARX) Neural Network." Water 13, no. 9 (2021): 1173. http://dx.doi.org/10.3390/w13091173.

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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

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Gopinath, Sumesh, and P. R. Prince. "Prediction of future evolution of solar cycle 24 using machine learning techniques." Proceedings of the International Astronomical Union 13, S340 (2018): 317–18. http://dx.doi.org/10.1017/s1743921318001485.

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AbstractForecasting the solar activity is of great importance not only for its effect on the climate of the Earth but also on the telecommunications, power lines, space missions and satellite safety. In the present work, machine learning using Artificial Neural Networks (ANNs) called Nonlinear Autoregressive Network (NAR) with Exogenous Inputs (NARX) have been applied for the prediction of future evolution of the present sunspot cycle. NARX network is able to combine the performance of ANN algorithm with nonlinear autoregressive method to handle problems such as finding dependencies among sola

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Talla Konchou, Franck Armel, Pascalin Tiam Kapen, Steve Brice Kenfack Magnissob, Mohamadou Youssoufa, and René Tchinda. "Prediction of wind speed profile using two artificial neural network models: an ab initio investigation in the Bapouh’s city, Cameroon." International Journal of Energy Sector Management 15, no. 3 (2021): 566–77. http://dx.doi.org/10.1108/ijesm-04-2020-0008.

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Purpose This paper aims to investigate the profile of the wind speed of a Cameroonian city for the very first time, as there is a growing trend for new wind energy installations in the West region of Cameroon. Two well-known artificial neural networks, namely, multi-layer perceptron (MLP) and nonlinear autoregressive network with exogenous inputs (NARX), were used to model the wind speed profile of the city of Bapouh in the West-region of Cameroon. Design/methodology/approach In this work, the profile of the wind speed of a Cameroonian city was investigated for the very first time since there

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Aquize, Rubén, Armando Cajahuaringa, José Machuca, David Mauricio, and Juan M. Mauricio Villanueva. "System Identification Methodology of a Gas Turbine Based on Artificial Recurrent Neural Networks." Sensors 23, no. 4 (2023): 2231. http://dx.doi.org/10.3390/s23042231.

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The application of identification techniques using artificial intelligence to the gas turbine (GT), whose nonlinear dynamic behavior is difficult to describe through differential equations and the laws of physics, has begun to gain importance for a little more than a decade. NARX (Nonlinear autoregressive network with exogenous inputs) is one of the models used to identify GT because it provides good results. However, existing studies need to show a systematic method to generate robust NARX models that can identify a GT with satisfactory accuracy. In this sense, a systematic method is proposed

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Tang, Zuhong. "Price And Replenishment of Vegetables Based on NARX Neural Network." Highlights in Business, Economics and Management 25 (January 20, 2024): 141–50. http://dx.doi.org/10.54097/9afh8v89.

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The research on the replenishment and pricing of vegetable commodities is helpful for supermarket to make more scientific purchase and sale decisions. Based on this, this paper mainly uses NARX neural network to study the replenishment and pricing of vegetable commodities. Firstly, it preprocesses the missing and abnormal values of the data, constructs a correlation analysis model to study the relationship between the sales of various categories of vegetables, then establishes a NARX neural network model to solve the daily replenishment total, and finally constructs a single objective programm

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Damos, Petros, José Tuells, and Pablo Caballero. "Soft Computing of a Medically Important Arthropod Vector with Autoregressive Recurrent and Focused Time Delay Artificial Neural Networks." Insects 12, no. 6 (2021): 503. http://dx.doi.org/10.3390/insects12060503.

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A central issue of public health strategies is the availability of decision tools to be used in the preventive management of the transmission cycle of vector-borne diseases. In this work, we present, for the first time, a soft system computing modeling approach using two dynamic artificial neural network (ANNs) models to describe and predict the non-linear incidence and time evolution of a medically important mosquito species, Culex sp., in Northern Greece. The first model is an exogenous non-linear autoregressive recurrent neural network (NARX), which is designed to take as inputs the tempera

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