Robust passivity analysis for uncertain neural networks with discrete and distributed time-varying delays

Chao Ge,Ju H Park,Changchun Hua,Caijuan Shi

doi:10.1016/j.neucom.2019.06.077

Robust passivity analysis for uncertain neural networks with discrete and distributed time-varying delays

Chao Ge, Ju H Park + Show 2 more

https://doi.org/10.1016/j.neucom.2019.06.077

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Journal: Neurocomputing	Publication Date: Jul 20, 2019
Citations: 24

Affiliation: Yeungnam University, Institute of Electrical Engineering, North China University of Science and Technology, Institute of Information Engineering

#Group Of Linear Matrix Inequalities #Class Of Uncertain Neural Networks + Show 8 more

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Abstract

In this paper, we address robust passivity analysis for a class of uncertain neural networks (NNs) with discrete and distributed time-varying delays. By selecting an improved Lyapunov-Krasovskii functional (LKF) with a novel delay-produce-type (DPT) term and combing free-matrix-based (FMB) integral inequality, some sufficient criteria are obtained to guarantee the passivity of uncertain NNs. Then, the maximal allowable upper bound (MAUB) of time-varying delay can be obtained by reciprocally convex combination (RCC) technique through solving a group of linear matrix inequalities (LMIs). Finally, numerical examples are considered to illustrate the benefit and superiority of the method proposed.

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