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Relevant bibliographies by topics / NARX Neural Network

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Academic literature on the topic 'NARX Neural Network'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "NARX Neural Network"

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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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Dissertations / Theses on the topic "NARX Neural Network"

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Kováčik, Juraj. "Využití umělé inteligence na kapitálových trzích." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2014. http://www.nusl.cz/ntk/nusl-224421.

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Ševčík, Martin. "Využití prostředků umělé inteligence pro podporu na kapitálových trzích." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2012. http://www.nusl.cz/ntk/nusl-223601.

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This diploma thesis describes issues of use of means of artificial intelligence for the decision making support on stock market. It includes theoretical knowledge of technical, fundamental and psychological analysis and artificial intelligence. Based on these facts have been created specific suggestions for the use of artificial neural networks to forecast the future value of the index S&P 500 by using development environment of the MATLAB software.

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Miklósy, Jiří. "Využití prostředků umělé inteligence na finančních trzích." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2013. http://www.nusl.cz/ntk/nusl-223952.

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Tato práce se zabývá návrhem, realizací a optimalizací systému určenímu k obchodování na finančních trzích, konkrétně s technologickými firmami trhu NASDAQ. K tomuto účelu jsou využívány technické indicatory a hlavně neuronových sítí. Vlastní řešení je pak realizováno v prostředi MATLAB.

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Vaško, Jan. "Využití prostředků umělé inteligence na kapitálových trzích." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2011. http://www.nusl.cz/ntk/nusl-222910.

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Diploma thesis deals with analyzing the possibility of using artificial intelligence, specifically artificial neural networks and fuzzy logic, on the capital markets as a tool to support decision making in business. The Matlab software is used for this purpose. The work is divided into three parts. The first part deals with theoretical knowledge, brief description of the current situationin is covered in a second part and the theoretical solutions are applied to the system in the third section.

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JÃnior, JosÃ Maria Pires de Menezes. "Redes neurais dinÃmicas para prediÃÃo e modelagem nÃo-linear de sÃries temporais." Universidade Federal do CearÃ, 2006. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=2090.

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Neste trabalho, redes neurais dinÃmicas sÃo avaliadas como modelos nÃo-lineares eficientes para prediÃÃo de sÃries temporais complexas. Entre as arquiteturas avaliadas estÃo as redes FTDNN, Elman e NARX. A capacidade preditiva destas redes sÃo testadas em tarefas de prediÃÃo de um-passo-adiante e mÃltiplos-passos-adiante. Para este fim, sÃo usadas as seguintes sÃries temporais: sÃrie laser caÃtico, sÃrie caÃtica Mackey-Glass, alÃm de sÃries de trÃfego de rede de computadores com caracterÃsticas auto-similares. O uso da rede NARX em prediÃÃo de sÃries temporais Ã uma contribuiÃÃo desta disserta

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Rocha, Renato Miguel Emílio. "Modelling of an electromagnetic generator using artificial neural networks." Master's thesis, Universidade de Aveiro, 2018. http://hdl.handle.net/10773/23608.

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Mestrado em Engenharia Mecânica<br>The increasing necessity for autonomous functioning systems alied with comsumption reduction by microelectronic devices over the last years, has motivated the research on self-powering devices for remote applications. Developing an energy harvestring device for a determined application requires its study and optimization. Therefore, modeling the dynamics of the system for simulation purposes becomes mandatory. The use of analytical mathematical models or FEM is a standard approach for the development of computable models. However, this approach reveals

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Santos, Tatiana Fernanda Mousquer dos. "APLICAÇÃO DE SÉRIES TEMPORAIS E REDES NEURAIS EM UM AMBIENTE DE COMPUTAÇÃO EM NUVEM." Universidade Federal de Santa Maria, 2014. http://repositorio.ufsm.br/handle/1/8316.

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Cloud computing has emerged to change the way computing is offered and used. Instead of having all the necessary hardware and software to manipulate and to store their data, users just need a mechanism to access the Internet. So, the efficient provisioning on demand of computational resources is a challenge to comply with the needs of users. Thus, there is a problem related to the lack of an underlying mechanism to assist a cloud management system to maintain acceptable levels of Quality of Service (QoS) pro-actively. In this context, this work makes a comparative analysis of the predictive ab

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Lajczyk, Pavel. "Využití umělé inteligence na kapitálových trzích." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2013. http://www.nusl.cz/ntk/nusl-223886.

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This master's thesis deals with artificial neural networks and possibilities of their use on stock market. In next chapters of this thesis there are provided design and implementation of stock prices prediction tool. The implementation is done with use of the MATLAB software. The created prediction tool is then tested in a simple trading simulation and achieved results are discussed in the end

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Skočík, Michal. "Využití umělé inteligence na kapitálových trzích." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2017. http://www.nusl.cz/ntk/nusl-318288.

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Diploma thesis is focused on problematics of artificial neural networks and their usage on capital markets. There is a software created as a part of this diploma thesis which can load input data and create neural network that serves for share price forecast. This program is created in numerical computing environment MATLAB. Created neural network is tested under simulation of business model. Results are discussed upon examination of results of simulation.

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Brnka, Radim. "Využití umělé inteligence na kapitálových trzích." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2012. http://www.nusl.cz/ntk/nusl-223594.

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The thesis deals with the design and optimization of artificial neural networks (specifically nonlinear autoregressive networks) and their subsequent usage in predictive application of stock market time series.

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Books on the topic "NARX Neural Network"

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Mangan, Michael, Tony J. Prescott, Minoru Asada, et al. Biomimetic and Biohybrid Systems: 8th International Conference, Living Machines 2019, Nara, Japan, July 9–12, 2019, Proceedings. Springer, 2019.

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Book chapters on the topic "NARX Neural Network"

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Chmielewski, Adrian, Jakub Możaryn, Piotr Piórkowski, and Krzysztof Bogdziński. "Battery Voltage Estimation Using NARX Recurrent Neural Network Model." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13273-6_22.

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Jiang, Jie, Fuge Wang, Yong Zhang, Yong Qin, and Xudong Gao. "Estimation of Vertical Track Irregularity Based on NARX Neural Network." In Proceedings of the 2015 International Conference on Electrical and Information Technologies for Rail Transportation. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49370-0_17.

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Zhang, Xiaoyu, Rui Wang, Tao Zhang, Ling Wang, Yajie Liu, and Yabing Zha. "Short-Term Load Forecasting Based on RBM and NARX Neural Network." In Intelligent Computing Methodologies. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95957-3_21.

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Peng, Jiao, and QingLong Tang. "Application of NARX Dynamic Neural Network in Quantitative Investment Forecasting System." In Communications in Computer and Information Science. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5577-0_50.

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Kováč, Szabolcs, Peter Střelec, Tibor Horák, German Michaľčonok, and Pavel Važan. "Forecasting Heat Production for a Large District Heating Network with NARX Neural Networks." In Artificial Intelligence Trends in Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09076-9_12.

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Calle, Miguel, Marcos Orellana, and Patricia Ortega-Chasi. "NARX Neural Network for Imputation of Missing Data in Air Pollution Datasets." In Information and Communication Technologies. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62833-8_18.

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Kumar, Sanjeeva, Santoshkumar Hampannavar, Abhishek Choudhary, and Swapna Mansani. "Short-term Load Forecasting: A Recurrent Dynamic Neural Network Approach Using NARX." In Advances in Data-Driven Computing and Intelligent Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3250-4_39.

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Vladymyrska, Nataliia, Michał Wróbel, Janusz T. Starczewski, and Viktoriia Hnatushenko. "NARX Neural Network for Prediction of Refresh Timeout in PIM–DM Multicast Routing." In Artificial Intelligence and Soft Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59063-9_18.

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Możaryn, Jakub. "NARX Recurrent Neural Network Model of the Graphene-Based Electronic Skin Sensors with Hysteretic Behaviour." In Digital Interaction and Machine Intelligence. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37649-8_23.

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AbstractThe electronic skin described in the article comprises screen-printed graphene-based sensors, intended to be used for robotic applications. The precise mathematical model allowing the touch pressure estimation is required during its calibration. The article describes the recurrent neural network model for graphene-based electronic skin calibration, in which parameters are not homogeneous, and the touch force characteristics have visible hysteretic behaviour. The presented method provides a simple alternative to the models known in the literature.

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Li, Xuefei, and Maoxiang Lang. "Combination Prediction of Railway Freight Volume Based on Support Vector Machine and NARX Neural Network." In LISS 2013. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40660-7_129.

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Conference papers on the topic "NARX Neural Network"

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Sahoo, Nihar K., Dhruba C. Panda, and Rabindra K. Mishra. "1D-FDTD Formulation with NARX Neural Network." In 2024 IEEE Microwaves, Antennas, and Propagation Conference (MAPCON). IEEE, 2024. https://doi.org/10.1109/mapcon61407.2024.10923038.

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Pham, Thuy T., Dang-Kièn G. Pham, Tayeb H. C. Bouazza, Pierre Almairac, and Patricia Desgreys. "NARX Neural Network Bandwidth Generalization Capability in Power Amplifier Modeling." In 2024 22nd IEEE Interregional NEWCAS Conference (NEWCAS). IEEE, 2024. http://dx.doi.org/10.1109/newcas58973.2024.10666110.

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Zhang, JunZhuang, Zhidan Yan, Tingting Song, and Zuodan Wang. "Continuous wave mud pulse channel equalization method based on NARX neural network." In Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), edited by Haiquan Zhao and Lei Chen. SPIE, 2025. https://doi.org/10.1117/12.3054410.

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Chen, Meng, and Ziyi Wang. "Parameter Identification of Permanent Magnet Synchronous Motors Based on NARX Neural Network." In 2024 13th International Conference of Information and Communication Technology (ICTech). IEEE, 2024. https://doi.org/10.1109/ictech63197.2024.00014.

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Toledo-Mercado, Esteban, and Ismael Soto. "Adaptive MRA Digital Filter using Wavelet, and NARX Neural Network for Ground-Based Transmission Spectroscopy." In 2024 IEEE International Conference on Automation/XXVI Congress of the Chilean Association of Automatic Control (ICA-ACCA). IEEE, 2024. http://dx.doi.org/10.1109/ica-acca62622.2024.10766763.

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Roslan, Muhammad Ikhsan, Noor Aida Syakira Ahmad Sabri, Nur Athirah Syafiqah Noramli, Nurlaila Ismail, Zakiah Mohd Yusoff, and Mohd Nasir Taib. "Enhancing NARX Neural Network for Agarwood Oil Grading: A Study on Resilient Backpropagation Training Method." In 2024 IEEE 22nd Student Conference on Research and Development (SCOReD). IEEE, 2024. https://doi.org/10.1109/scored64708.2024.10872669.

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Song Liu, Xuemiao Pang, Haiyan Ji, and Hao Chen. "Prediction of track irregularities using NARX neural network." In 2010 Second Pacific-Asia Conference on Circuits,Communications and System (PACCS). IEEE, 2010. http://dx.doi.org/10.1109/paccs.2010.5627046.

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Putri, Adi Novitarini, Carmadi Machbub, and Egi Muhammad Idris Hidayat. "NARX Neural Network Modeling of Batch Distillation Process." In 2021 IEEE 11th International Conference on System Engineering and Technology (ICSET). IEEE, 2021. http://dx.doi.org/10.1109/icset53708.2021.9612562.

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Suchacka, Grazyna, Daria Wotzka, Lukasz Mach, Pawel Fracz, Marzena Stec, and Joachim Foltys. "Analysis of the housing market dynamics using narx neural network." In 38th ECMS International Conference on Modelling and Simulation. ECMS, 2024. http://dx.doi.org/10.7148/2024-0084.

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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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Siek, Michael, and Dimitri Solomatine. "Chaotic model with data assimilation using NARX network." In 2009 International Joint Conference on Neural Networks (IJCNN 2009 - Atlanta). IEEE, 2009. http://dx.doi.org/10.1109/ijcnn.2009.5178940.

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