New global asymptotic stability criterion for neural networks with discrete and distributed delays

Peng Shi; Jinhui Zhang; Jiqing Qiu; Li'nan Xing

doi:10.1243/09596518JSCE287

New global asymptotic stability criterion for neural networks with discrete and distributed delays

Peng Shi, Jinhui Zhang, Jiqing Qiu^*, Li'nan Xing

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

21 Citations (Scopus)

Abstract

This paper investigates the problem of global asymptotic stability for a class of neural networks with time-varying and distributed delays. By the Lyapunov-Krasovskii functional approach, a new delay-dependent stability criterion is derived in terms of linear matrix inequalities (LMIs). The new stability condition does not require the time delay function to be continuously differenliable and its derivative to be less than 1, and it allows the time delay to be a fast time-varying function. Simulation examples are given to demonstrate the effectiveness of the developed techniques.

Original language	English
Pages (from-to)	129-135
Number of pages	7
Journal	Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering
Volume	221
Issue number	1
DOIs	https://doi.org/10.1243/09596518JSCE287
Publication status	Published - Jan 2007
Externally published	Yes

Keywords

Distributed delay
Global asymptotic stability
Linear matrix inequalities
Neural networks
Time-varying delay

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10.1243/09596518JSCE287

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abstract = "This paper investigates the problem of global asymptotic stability for a class of neural networks with time-varying and distributed delays. By the Lyapunov-Krasovskii functional approach, a new delay-dependent stability criterion is derived in terms of linear matrix inequalities (LMIs). The new stability condition does not require the time delay function to be continuously differenliable and its derivative to be less than 1, and it allows the time delay to be a fast time-varying function. Simulation examples are given to demonstrate the effectiveness of the developed techniques.",

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AB - This paper investigates the problem of global asymptotic stability for a class of neural networks with time-varying and distributed delays. By the Lyapunov-Krasovskii functional approach, a new delay-dependent stability criterion is derived in terms of linear matrix inequalities (LMIs). The new stability condition does not require the time delay function to be continuously differenliable and its derivative to be less than 1, and it allows the time delay to be a fast time-varying function. Simulation examples are given to demonstrate the effectiveness of the developed techniques.

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KW - Global asymptotic stability

KW - Linear matrix inequalities

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KW - Time-varying delay

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JF - Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering

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ER -

New global asymptotic stability criterion for neural networks with discrete and distributed delays

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Keywords

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