Neural network online decoupling for a class of nonlinear system

Xinli Li; Yan Bai; Lin Yang

doi:10.1109/WCICA.2006.1712900

Neural network online decoupling for a class of nonlinear system

Xinli Li^*, Yan Bai, Lin Yang

^*Corresponding author for this work

School of Mechanical Engineering

North China Electric Power University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

11 Citations (Scopus)

Abstract

Aim at a class of nonlinear MIMO systems, the neural networks online decoupling algorithm is proposed. The elitist genetic algorithms and hybrid genetic algorithms are adopted respectively to train the neural networks in order to compensate coupling effect. Based on analysis of the convergence of the genetic algorithms, the feasibility of the online decoupling algorithm is discussed. The effectiveness of the algorithm has been shown by numerical simulations combing nonlinear MIMO system.

Original language	English
Title of host publication	Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
Pages	2920-2924
Number of pages	5
DOIs	https://doi.org/10.1109/WCICA.2006.1712900
Publication status	Published - 2006
Event	6th World Congress on Intelligent Control and Automation, WCICA 2006 - Dalian, China Duration: 21 Jun 2006 → 23 Jun 2006

Publication series

Name	Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
Volume	1

Conference

Conference	6th World Congress on Intelligent Control and Automation, WCICA 2006
Country/Territory	China
City	Dalian
Period	21/06/06 → 23/06/06

Keywords

Convergence
Genetic algorithm
Neural network
Nonlinear system
Online decoupling

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10.1109/WCICA.2006.1712900

Cite this

Li, X., Bai, Y., & Yang, L. (2006). Neural network online decoupling for a class of nonlinear system. In Proceedings of the World Congress on Intelligent Control and Automation (WCICA) (pp. 2920-2924). Article 1712900 (Proceedings of the World Congress on Intelligent Control and Automation (WCICA); Vol. 1). https://doi.org/10.1109/WCICA.2006.1712900

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title = "Neural network online decoupling for a class of nonlinear system",

abstract = "Aim at a class of nonlinear MIMO systems, the neural networks online decoupling algorithm is proposed. The elitist genetic algorithms and hybrid genetic algorithms are adopted respectively to train the neural networks in order to compensate coupling effect. Based on analysis of the convergence of the genetic algorithms, the feasibility of the online decoupling algorithm is discussed. The effectiveness of the algorithm has been shown by numerical simulations combing nonlinear MIMO system.",

keywords = "Convergence, Genetic algorithm, Neural network, Nonlinear system, Online decoupling",

author = "Xinli Li and Yan Bai and Lin Yang",

year = "2006",

doi = "10.1109/WCICA.2006.1712900",

language = "English",

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series = "Proceedings of the World Congress on Intelligent Control and Automation (WCICA)",

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note = "6th World Congress on Intelligent Control and Automation, WCICA 2006 ; Conference date: 21-06-2006 Through 23-06-2006",

}

Li, X, Bai, Y & Yang, L 2006, Neural network online decoupling for a class of nonlinear system. in Proceedings of the World Congress on Intelligent Control and Automation (WCICA)., 1712900, Proceedings of the World Congress on Intelligent Control and Automation (WCICA), vol. 1, pp. 2920-2924, 6th World Congress on Intelligent Control and Automation, WCICA 2006, Dalian, China, 21/06/06. https://doi.org/10.1109/WCICA.2006.1712900

Neural network online decoupling for a class of nonlinear system. / Li, Xinli; Bai, Yan; Yang, Lin.
Proceedings of the World Congress on Intelligent Control and Automation (WCICA). 2006. p. 2920-2924 1712900 (Proceedings of the World Congress on Intelligent Control and Automation (WCICA); Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Li, Xinli

AU - Bai, Yan

AU - Yang, Lin

PY - 2006

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N2 - Aim at a class of nonlinear MIMO systems, the neural networks online decoupling algorithm is proposed. The elitist genetic algorithms and hybrid genetic algorithms are adopted respectively to train the neural networks in order to compensate coupling effect. Based on analysis of the convergence of the genetic algorithms, the feasibility of the online decoupling algorithm is discussed. The effectiveness of the algorithm has been shown by numerical simulations combing nonlinear MIMO system.

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KW - Convergence

KW - Genetic algorithm

KW - Neural network

KW - Nonlinear system

KW - Online decoupling

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Neural network online decoupling for a class of nonlinear system

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Conference

Keywords

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