A neural-network approach to modeling and analysis

Chen Yuan Chen; Cheng Wu Chen; Wei Ling Chiang; Jing Dong Hwang

A neural-network approach to modeling and analysis

Chen Yuan Chen, Cheng Wu Chen, Wei Ling Chiang, Jing Dong Hwang

Department of Civil Engineering

Research output: Contribution to journal › Conference article › peer-review

2 Scopus citations

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 asymptotic stability of NN systems.

Original language	English
Pages (from-to)	489-493
Number of pages	5
Journal	Proceedings of the International Conference on Tools with Artificial Intelligence
State	Published - 2002
Event	14th International Conference on Tools with Artificial Intelligence - Washington, DC, United States Duration: 4 Jun 2002 → 6 Nov 2002

Keywords

Lyapunov theory
Neural network

Cite this

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title = "A neural-network approach to modeling and analysis",

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 asymptotic stability of NN systems.",

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T1 - A neural-network approach to modeling and analysis

AU - Chen, Chen Yuan

AU - Chen, Cheng Wu

AU - Chiang, Wei Ling

AU - Hwang, Jing Dong

PY - 2002

Y1 - 2002

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 asymptotic stability of NN systems.

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 asymptotic stability of NN systems.

KW - Lyapunov theory

KW - Neural network

UR - http://www.scopus.com/inward/record.url?scp=0036928589&partnerID=8YFLogxK

M3 - 會議論文

AN - SCOPUS:0036928589

SN - 1063-6730

SP - 489

EP - 493

JO - Proceedings of the International Conference on Tools with Artificial Intelligence

JF - Proceedings of the International Conference on Tools with Artificial Intelligence

T2 - 14th International Conference on Tools with Artificial Intelligence

Y2 - 4 June 2002 through 6 November 2002

ER -

A neural-network approach to modeling and analysis

Abstract

Keywords

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