Further improvement of the Lyapunov functional and the delay-dependent stability criterion for a neural network with a constant delay

This paper investigates the asymptotical stability problem of a neural system with a constant delay. A new delay-dependent stability condition is derived by using the novel augmented Lyapunov-Krasovskii function with triple integral terms, and the additional triple integral terms play a key role in...

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Bibliographic Details
Published inChinese physics B Vol. 21; no. 4; pp. 179 - 184
Main Author 邱芳 张全信 邓学辉
Format Journal Article
LanguageEnglish
Published 01.04.2012
Subjects
Asymptotic properties
Constants
Criteria
Delay
Integrals
Lyapunov-Krasovskii函数
Lyapunov泛函
Mathematical analysis
Mathematical models
Neural networks
Stability
三重积分
延迟
恒定
时滞相关
神经网络
稳定性判据
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Summary:This paper investigates the asymptotical stability problem of a neural system with a constant delay. A new delay-dependent stability condition is derived by using the novel augmented Lyapunov-Krasovskii function with triple integral terms, and the additional triple integral terms play a key role in the further reduction of conservativeness. Finally, a numerical example is given to demonstrate the effectiveness and lower conservativeness of the proposed method.
Bibliography:This paper investigates the asymptotical stability problem of a neural system with a constant delay. A new delay-dependent stability condition is derived by using the novel augmented Lyapunov-Krasovskii function with triple integral terms, and the additional triple integral terms play a key role in the further reduction of conservativeness. Finally, a numerical example is given to demonstrate the effectiveness and lower conservativeness of the proposed method.
11-5639/O4
neural system, globally asymptotical stability, time delay
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1674-1056
2058-3834
1741-4199
DOI:10.1088/1674-1056/21/4/040701