Global asymptotic stability analysis of discrete-time Cohen-Grossberg neural networks based on interval systems

Meiqin Liu

doi:10.1016/j.na.2007.08.019

Global asymptotic stability analysis of discrete-time Cohen-Grossberg neural networks based on interval systems

Meiqin Liu

Zhejiang University

科研成果: 期刊稿件 › 文章 › 同行评审

19 引用（Scopus）

摘要

The global asymptotic stability of discrete-time Cohen-Grossberg neural networks (CGNNs) with or without time delays is studied in this paper. The CGNNs are transformed into discrete-time interval systems, and several sufficient conditions for asymptotic stability for these interval systems are derived by constructing some suitable Lyapunov functionals. The conditions obtained are given in the form of linear matrix inequalities that can be checked numerically and very efficiently by using the MATLAB LMI Control Toolbox. Finally, some illustrative numerical examples are provided to demonstrate the effectiveness of the results obtained.

源语言	英语
页（从-至）	2403-2411
页数	9
期刊	Nonlinear Analysis, Theory, Methods and Applications
卷	69
期	8
DOI	https://doi.org/10.1016/j.na.2007.08.019
出版状态	已出版 - 15 10月 2008
已对外发布	是

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abstract = "The global asymptotic stability of discrete-time Cohen-Grossberg neural networks (CGNNs) with or without time delays is studied in this paper. The CGNNs are transformed into discrete-time interval systems, and several sufficient conditions for asymptotic stability for these interval systems are derived by constructing some suitable Lyapunov functionals. The conditions obtained are given in the form of linear matrix inequalities that can be checked numerically and very efficiently by using the MATLAB LMI Control Toolbox. Finally, some illustrative numerical examples are provided to demonstrate the effectiveness of the results obtained.",

keywords = "Cohen-Grossberg neural network, Discrete time, Global asymptotic stability, Interval system, Linear matrix inequality (LMI), Time-delay system",

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N2 - The global asymptotic stability of discrete-time Cohen-Grossberg neural networks (CGNNs) with or without time delays is studied in this paper. The CGNNs are transformed into discrete-time interval systems, and several sufficient conditions for asymptotic stability for these interval systems are derived by constructing some suitable Lyapunov functionals. The conditions obtained are given in the form of linear matrix inequalities that can be checked numerically and very efficiently by using the MATLAB LMI Control Toolbox. Finally, some illustrative numerical examples are provided to demonstrate the effectiveness of the results obtained.

AB - The global asymptotic stability of discrete-time Cohen-Grossberg neural networks (CGNNs) with or without time delays is studied in this paper. The CGNNs are transformed into discrete-time interval systems, and several sufficient conditions for asymptotic stability for these interval systems are derived by constructing some suitable Lyapunov functionals. The conditions obtained are given in the form of linear matrix inequalities that can be checked numerically and very efficiently by using the MATLAB LMI Control Toolbox. Finally, some illustrative numerical examples are provided to demonstrate the effectiveness of the results obtained.

KW - Cohen-Grossberg neural network

KW - Discrete time

KW - Global asymptotic stability

KW - Interval system

KW - Linear matrix inequality (LMI)

KW - Time-delay system

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JO - Nonlinear Analysis, Theory, Methods and Applications

JF - Nonlinear Analysis, Theory, Methods and Applications

IS - 8

ER -

Global asymptotic stability analysis of discrete-time Cohen-Grossberg neural networks based on interval systems

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