Delay-difference-dependent robust exponential stability for uncertain stochastic neural networks with multiple delays

This paper deals with the problem of robust exponential stability for a class of uncertain stochastic neural networks with multiple delays. Based on the multiple-difference-dependent Lyapunov–Krasovskii functional and free-weighting matrices method, some novel stability criteria for the addressed un...

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Bibliographic Details
Published inNeurocomputing (Amsterdam) Vol. 140; pp. 210 - 218
Main Authors Xia, Jianwei, Park, Ju H., Zeng, Hongbing, Shen, Hao
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 22.09.2014
Elsevier
Subjects
Adaptative systems
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Connectionism. Neural networks
Control system analysis
Control theory. Systems
Exact sciences and technology
Exponential stability
LMIs
Multiple-delays
Stochastic neural networks
Stochastic neural networks
Multiple-delays
LMIs
Exponential stability
Robust stability
Probabilistic approach
Multiple delay
Delay system
Stability criterion
Neural network
Stochastic process
Robust control
Uncertain system
Linear matrix inequality
Robustness
Lyapunov function
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Summary:This paper deals with the problem of robust exponential stability for a class of uncertain stochastic neural networks with multiple delays. Based on the multiple-difference-dependent Lyapunov–Krasovskii functional and free-weighting matrices method, some novel stability criteria for the addressed uncertain stochastic neural networks are derived. At last, two numerical examples are presented to show the effectiveness and improvement of the proposed results.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2014.03.022