Exponential passivity for uncertain neural networks with time-varying delays based on weighted integral inequalities

被引：7

作者：

Saravanan, S. ^{[1
]}

Umesha, V ^{[2
]}

Ali, M. Syed ^{[1
]}

Padmanabhan, S. ^{[3
]}

机构：

[1] Thiruvalluvar Univ, Dept Math, Vellore 632115, Tamil Nadu, India

[2] Dayananada Sagar Coll Engn, Dept Math, Bangalore 560078, Karnataka, India

[3] RNS Inst Technol, Dept Math, Bangalore 560098, Karnataka, India

来源：

NEUROCOMPUTING | 2018年 / 314卷

关键词：

Lyapunov-Krasovskii functional (LKF); Neural networks; Passivity; Time-varying delays; Uncertainty; Weighted integral inequalities; MARKOVIAN JUMPING PARAMETERS; SLIDING MODE APPROACH; STABILITY ANALYSIS; NEUTRAL-TYPE; STATE ESTIMATION; DISTRIBUTED DELAYS; LMI APPROACH; SYSTEMS; DISCRETE; SYNCHRONIZATION;

D O I：

10.1016/j.neucom.2018.07.009

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper is concerned with the problem of an exponential passivity analysis for uncertain neural networks with time-varying delays. By constructing an appropriate Lyapunov-Krasovskii functional and using the weighted integral inequality techniques to estimate its derivative. We established a sufficient criterion such that, for all admissible parameter uncertainties, the neural network is exponentially passive. The derived criteria are expressed in the terms of linear matrix inequalities (LMIs), that can be easily checked by using the standard numerical software. Illustrative examples are presented to demonstrate the effectiveness and usefulness of the proposed results. (C) 2018 Elsevier B.V. All rights reserved.

引用

页码：429 / 436

页数：8

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