Robust stability analysis of interval fuzzy Cohen-Grossberg neural networks with piecewise constant argument of generalized type

被引：77

作者：

Bao, Gang

Wen, Shiping

Zeng, Zhigang ^{[1
]}

机构：

[1] Huazhong Univ Sci & Technol, Dept Control Sci & Engn, Wuhan 430074, Peoples R China

来源：

NEURAL NETWORKS | 2012年 / 33卷

关键词：

Piecewise constant argument; Fuzzy recurrent neural networks; Global robustly exponential stability; TIME-VARYING DELAYS; GLOBAL EXPONENTIAL STABILITY; DIFFERENTIAL-EQUATIONS; ASSOCIATIVE MEMORIES; ASYMPTOTIC STABILITY; DIFFUSION;

D O I：

10.1016/j.neunet.2012.04.003

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, existence and uniqueness of the solution of interval fuzzy Cohen-Grossberg neural networks with piecewise constant argument are discussed. Based on the comparison principle, it presents new theoretical results on the global robust exponential stability of interval fuzzy Cohen-Grossberg networks with piecewise constant argument. As a special case, the corresponding results of interval fuzzy recurrent neural networks with piecewise constant argument are derived. Three examples are given for illustrating validity of the obtained results. (c) 2012 Elsevier Ltd. All rights reserved.

引用

页码：32 / 41

页数：10

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