Auxiliary function-based integral inequality approach to robust passivity analysis of neural networks with interval time-varying delay

NEUROCOMPUTING

卷:306

Article

Zhang, Fen^1,2  Li, Zhi¹ 

[1] Xidian Univ, Dept Automat Control, Xian 710071, Shaanxi, Peoples R China

[2] Xianyang Normal Univ, Coll Math & Informat Sci, Xianyang 712000, Shaanxi, Peoples R China

关键词: Neural networks;    Parameter uncertainties;    Passivity;    Time-varying delays;    Lyapunov-Krasovskii functional (LKF);   

DOI  :  10.1016/j.neucom.2018.04.026

来源: Elsevier

【 摘 要 】

In this paper, we study the problem of passivity for uncertain neural networks with interval time-varying delay. Firstly, a suitable augmented Lyapunov-Krasovskii functional (LKF) containing two triple integral terms is constructed and an auxiliary function-based integral inequality (AFBI) is used to manipulate the augmented single integral terms in the derivative of LKF. Secondly, a special form of the AFBI is applied to deal with the delay-product-type term, which was used to be ignored in the time derivative of a triple integral term. As a result, less conservative delay-dependent passivity criteria are derived for normal delayed neural networks (DNNs) in the form of linear matrix inequalities (LMIs). In addition, with the same LKF, delay-dependent passivity criteria are obtained for normal DNNs without the delay-producttype term. Subsequently, these criteria are extended to DNNs with parameter uncertainties. Finally, four numerical examples and simulations are provided to illustrate the effectiveness of the proposed criteria. (C) 2018 Published by Elsevier B.V.

【 授权许可】

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