An improved stability result for delayed Takagi-Sugeno fuzzy Cohen-Grossberg neural networks

被引：10

作者：

Orman, Zeynep ^{[1
]}

机构：

[1] Istanbul Univ Cerrahpasa, Fac Engn, Dept Comp Engn, TR-34320 Istanbul, Turkey

来源：

NEURAL NETWORKS | 2018年 / 108卷

关键词：

Cohen-Grossberg neural systems; Stability analysis; Fuzzy theory; Time delays; ROBUST EXPONENTIAL STABILITY; ACTIVATION FUNCTIONS; TIME-DELAYS; MULTISTABILITY; DESIGN; SYSTEM;

D O I：

10.1016/j.neunet.2018.09.008

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This work proposes a novel and improved delay independent global asymptotic stability criterion for delayed Takagi-Sugeno (T-S) fuzzy Cohen-Grossberg neural networks exploiting a suitable fuzzy-type Lyapunov functional in the presence of the nondecreasing activation functions having bounded slopes. The proposed stability criterion can be easily validated as it is completely expressed in terms of the system matrices of the fuzzy neural network model considered. It will be shown that the stability criterion obtained in this work for this type of fuzzy neural networks improves and generalizes some of the previously published stability results. A constructive numerical example is also given to support the proposed theoretical results. (c) 2018 Elsevier Ltd. All rights reserved.

引用

页码：445 / 451

页数：7

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