Fuzzy control for nonlinear systems modeled via neural-network

Zhen Yuan Chen; Cheng Wu Chen; Wei Ling Chiang; Jiing Don Huang

doi:10.1109/ICIT.2002.1189863

Fuzzy control for nonlinear systems modeled via neural-network

Zhen Yuan Chen, Cheng Wu Chen, Wei Ling Chiang, Jiing Don Huang

Department of Civil Engineering

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

Abstract

It is known that backpropagation network is always used to modeling. This study is concerned with the stability problem of a neural network (NN) system which consists of a few subsystems represented by NN models. In this paper, the dynamics of each NN model is converted into linear inclusion representation. Subsequently, based on the representations, the stability conditions in terms of Lyapunov's direct method is derived to guarantee the stability of nonlinear systems. Finally, a numerical example with simulations is given to illustrate the results.

Original language	English
Title of host publication	IEEE ICIT 2002 - 2002 IEEE International Conference on Industrial Technology
Subtitle of host publication	"Productivity Reincarnation Through Robotics and Automation"
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	66-71
Number of pages	6
ISBN (Electronic)	0780376579
DOIs	https://doi.org/10.1109/ICIT.2002.1189863
State	Published - 2002
Event	IEEE International Conference on Industrial Technology, IEEE ICIT 2002 - Bangkok, Thailand Duration: 11 Dec 2002 → 14 Dec 2002

Publication series

Name	Proceedings of the IEEE International Conference on Industrial Technology
Volume	1

Conference

Conference	IEEE International Conference on Industrial Technology, IEEE ICIT 2002
Country/Territory	Thailand
City	Bangkok
Period	11/12/02 → 14/12/02

Access to Document

10.1109/ICIT.2002.1189863

Cite this

Chen, Z. Y., Chen, C. W., Chiang, W. L., & Huang, J. D. (2002). Fuzzy control for nonlinear systems modeled via neural-network. In IEEE ICIT 2002 - 2002 IEEE International Conference on Industrial Technology: "Productivity Reincarnation Through Robotics and Automation" (pp. 66-71). [1189863] (Proceedings of the IEEE International Conference on Industrial Technology; Vol. 1). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIT.2002.1189863

Chen, Zhen Yuan ; Chen, Cheng Wu ; Chiang, Wei Ling ; Huang, Jiing Don. / Fuzzy control for nonlinear systems modeled via neural-network. IEEE ICIT 2002 - 2002 IEEE International Conference on Industrial Technology: "Productivity Reincarnation Through Robotics and Automation". Institute of Electrical and Electronics Engineers Inc., 2002. pp. 66-71 (Proceedings of the IEEE International Conference on Industrial Technology).

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title = "Fuzzy control for nonlinear systems modeled via neural-network",

abstract = "It is known that backpropagation network is always used to modeling. This study is concerned with the stability problem of a neural network (NN) system which consists of a few subsystems represented by NN models. In this paper, the dynamics of each NN model is converted into linear inclusion representation. Subsequently, based on the representations, the stability conditions in terms of Lyapunov's direct method is derived to guarantee the stability of nonlinear systems. Finally, a numerical example with simulations is given to illustrate the results.",

author = "Chen, {Zhen Yuan} and Chen, {Cheng Wu} and Chiang, {Wei Ling} and Huang, {Jiing Don}",

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Chen, ZY, Chen, CW, Chiang, WL & Huang, JD 2002, Fuzzy control for nonlinear systems modeled via neural-network. in IEEE ICIT 2002 - 2002 IEEE International Conference on Industrial Technology: "Productivity Reincarnation Through Robotics and Automation"., 1189863, Proceedings of the IEEE International Conference on Industrial Technology, vol. 1, Institute of Electrical and Electronics Engineers Inc., pp. 66-71, IEEE International Conference on Industrial Technology, IEEE ICIT 2002, Bangkok, Thailand, 11/12/02. https://doi.org/10.1109/ICIT.2002.1189863

Fuzzy control for nonlinear systems modeled via neural-network. / Chen, Zhen Yuan; Chen, Cheng Wu; Chiang, Wei Ling; Huang, Jiing Don.

IEEE ICIT 2002 - 2002 IEEE International Conference on Industrial Technology: "Productivity Reincarnation Through Robotics and Automation". Institute of Electrical and Electronics Engineers Inc., 2002. p. 66-71 1189863 (Proceedings of the IEEE International Conference on Industrial Technology; Vol. 1).

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

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AU - Chen, Cheng Wu

AU - Chiang, Wei Ling

AU - Huang, Jiing Don

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N2 - It is known that backpropagation network is always used to modeling. This study is concerned with the stability problem of a neural network (NN) system which consists of a few subsystems represented by NN models. In this paper, the dynamics of each NN model is converted into linear inclusion representation. Subsequently, based on the representations, the stability conditions in terms of Lyapunov's direct method is derived to guarantee the stability of nonlinear systems. Finally, a numerical example with simulations is given to illustrate the results.

AB - It is known that backpropagation network is always used to modeling. This study is concerned with the stability problem of a neural network (NN) system which consists of a few subsystems represented by NN models. In this paper, the dynamics of each NN model is converted into linear inclusion representation. Subsequently, based on the representations, the stability conditions in terms of Lyapunov's direct method is derived to guarantee the stability of nonlinear systems. Finally, a numerical example with simulations is given to illustrate the results.

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T3 - Proceedings of the IEEE International Conference on Industrial Technology

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Chen ZY, Chen CW, Chiang WL, Huang JD. Fuzzy control for nonlinear systems modeled via neural-network. In IEEE ICIT 2002 - 2002 IEEE International Conference on Industrial Technology: "Productivity Reincarnation Through Robotics and Automation". Institute of Electrical and Electronics Engineers Inc. 2002. p. 66-71. 1189863. (Proceedings of the IEEE International Conference on Industrial Technology). https://doi.org/10.1109/ICIT.2002.1189863

Fuzzy control for nonlinear systems modeled via neural-network

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