Robustness Analysis of Fuzzy Cellular Neural Network With Deviating Argument and Stochastic Disturbances

被引：11

作者：

Fang, Wenxiang ^{[1
]}

Xie, Tao ^{[1
]}

LI, Biwen ^{[1
]}

机构：

[1] Hubei Normal Univ, Sch Math & Stat, Huangshi 435002, Hubei, Peoples R China

来源：

IEEE ACCESS | 2023年 / 11卷

关键词：

Stochastic processes; Robustness; Cellular neural networks; Stability criteria; Artificial neural networks; Upper bound; Mathematical models; Fuzzy systems; Cellular networks; Neural networks; Fuzzy cellular neural network; robustness analysis; deviating argument; stochastic disturbances; STABILITY ANALYSIS; EXPONENTIAL STABILITY; CONVERGENCE; SYSTEM;

D O I：

10.1109/ACCESS.2023.3233946

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Robustness analysis of fuzzy cellular neural networks with deviating arguments and stochastic disturbances is the main topic of discussion in this paper. The issue at hand is what the upper bounds of the disturbances and deviating intervals for the fuzzy cellular neural network can withstand before losing its stability. We solve these problems by using Gronwall-Bellman lemma and some inequality techniques. The theoretical results point that for an exponentially stable fuzzy cellular neural network, the perturbed fuzzy cellular neural network still keep its globally exponential stability if the upper bound of the length of deviating intervals or the intensity of stochastic disturbances is less than the upper bound derived in this paper. A number of numerical cases are offered to support the availability of conjectural values.

引用

页码：3717 / 3728

页数：12

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