Exponential stability analysis for neural networks with time-varying delay

Min Wu; Fang Liu; Peng Shi; Yong He; Ryuichi Yokoyama

doi:10.1109/TSMCB.2008.915652

Exponential stability analysis for neural networks with time-varying delay

Min Wu, Fang Liu, Peng Shi, Yong He^*, Ryuichi Yokoyama

^*Corresponding author for this work

Waseda University

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

92 Citations (Scopus)

Abstract

This correspondence paper focuses on the problem of exponential stability for neural networks with a time-varying delay. The relationship among the time-varying delay, its upper bound, and their difference is taken into account. As a result, an improved linear-matrix-inequality-based delay-dependent exponential stability criterion is obtained without ignoring any terms in the derivative of Lyapunov-Krasovskii functional. Two numerical examples are given to demonstrate its effectiveness.

Original language	English
Pages (from-to)	1152-1156
Number of pages	5
Journal	IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume	38
Issue number	4
DOIs	https://doi.org/10.1109/TSMCB.2008.915652
Publication status	Published - 2008 Aug

Keywords

Exponential stability
Linear matrix inequality (LMI)
Neural networks
Time-varying delay

ASJC Scopus subject areas

Control and Systems Engineering
Artificial Intelligence
Human-Computer Interaction
Medicine(all)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/TSMCB.2008.915652

Cite this

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AU - Shi, Peng

AU - He, Yong

AU - Yokoyama, Ryuichi

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N2 - This correspondence paper focuses on the problem of exponential stability for neural networks with a time-varying delay. The relationship among the time-varying delay, its upper bound, and their difference is taken into account. As a result, an improved linear-matrix-inequality-based delay-dependent exponential stability criterion is obtained without ignoring any terms in the derivative of Lyapunov-Krasovskii functional. Two numerical examples are given to demonstrate its effectiveness.

AB - This correspondence paper focuses on the problem of exponential stability for neural networks with a time-varying delay. The relationship among the time-varying delay, its upper bound, and their difference is taken into account. As a result, an improved linear-matrix-inequality-based delay-dependent exponential stability criterion is obtained without ignoring any terms in the derivative of Lyapunov-Krasovskii functional. Two numerical examples are given to demonstrate its effectiveness.

KW - Exponential stability

KW - Linear matrix inequality (LMI)

KW - Neural networks

KW - Time-varying delay

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Exponential stability analysis for neural networks with time-varying delay

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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