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Article

Neural Computing Enhanced Parameter Estimation for Multi-Input and Multi-Output Total Non-Linear Dynamic Models

1
School of Mathematical Sciences, Ocean University of China, Qingdao 266000, China
2
Robotics and Internet-of-Things Lab (RIOTU), Prince Sultan University, Riyadh 11586, Saudi Arabia
3
Faculty of Computers and Artificial Intelligence, Benha University, 13511 Benha, Egypt
4
Department of Engineering Design and Mathematics, University of the West of England, Frenchy Campus Coldharbour Lane, Bristol BS16 1QY, UK
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(5), 510; https://doi.org/10.3390/e22050510
Received: 28 March 2020 / Revised: 24 April 2020 / Accepted: 26 April 2020 / Published: 30 April 2020
In this paper, a gradient descent algorithm is proposed for the parameter estimation of multi-input and multi-output (MIMO) total non-linear dynamic models. Firstly, the MIMO total non-linear model is mapped to a non-completely connected feedforward neural network, that is, the parameters of the total non-linear model are mapped to the connection weights of the neural network. Then, based on the minimization of network error, a weight-updating algorithm, that is, an estimation algorithm of model parameters, is proposed with the convergence conditions of a non-completely connected feedforward network. In further determining the variables of the model set, a method of model structure detection is proposed for selecting a group of important items from the whole variable candidate set. In order to verify the usefulness of the parameter identification process, we provide a virtual bench test example for the numerical analysis and user-friendly instructions for potential applications. View Full-Text
Keywords: parameter estimation; total non-linear model; neural networks; neuro-computing; gradient descent algorithm parameter estimation; total non-linear model; neural networks; neuro-computing; gradient descent algorithm
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MDPI and ACS Style

Liu, L.; Ma, D.; Azar, A.T.; Zhu, Q. Neural Computing Enhanced Parameter Estimation for Multi-Input and Multi-Output Total Non-Linear Dynamic Models. Entropy 2020, 22, 510. https://doi.org/10.3390/e22050510

AMA Style

Liu L, Ma D, Azar AT, Zhu Q. Neural Computing Enhanced Parameter Estimation for Multi-Input and Multi-Output Total Non-Linear Dynamic Models. Entropy. 2020; 22(5):510. https://doi.org/10.3390/e22050510

Chicago/Turabian Style

Liu, Longlong, Di Ma, Ahmad Taher Azar, and Quanmin Zhu. 2020. "Neural Computing Enhanced Parameter Estimation for Multi-Input and Multi-Output Total Non-Linear Dynamic Models" Entropy 22, no. 5: 510. https://doi.org/10.3390/e22050510

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