Further results on passivity analysis for uncertain neural networks with discrete and distributed delays

The problem of passivity analysis of uncertain neural networks (UNNs) with discrete and distributed delay is considered. By constructing a suitable augmented Lyapunov-Krasovskii functional(LKF) and combing a novel integral inequality with convex approach to estimate the derivative of the proposed LK...

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Bibliographic Details
Published inInformation sciences Vol. 430-431; pp. 77 - 86
Main Authors Yang, Bin, Wang, Juan, Hao, Mengnan, Zeng, Hongbing
Format Journal Article
LanguageEnglish
Published Elsevier Inc 01.03.2018
Subjects
Delay
Integral inequality
Lyapunov-Krasovskii functional
Neural networks
Passivity
Lyapunov-Krasovskii functional
Integral inequality
Neural networks
Passivity
Delay
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Summary:The problem of passivity analysis of uncertain neural networks (UNNs) with discrete and distributed delay is considered. By constructing a suitable augmented Lyapunov-Krasovskii functional(LKF) and combing a novel integral inequality with convex approach to estimate the derivative of the proposed LKF, improved sufficient conditions to guarantee passivity of the concerned neural networks are established with the framework of linear matrix inequalities(LMIs), which can be solved easily by various efficient convex optimization algorithms. Two numerical examples are provided to demonstrate the enhancement of feasible region of the proposed criteria by the comparison of maximum allowable delay bounds.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2017.11.015