Exponential Stability and Periodicity of Fuzzy Delayed Reaction-Diffusion Cellular Neural Networks with Impulsive Effect

被引：5

作者：

Yang, Guowei ^{[1
]}

Kao, Yonggui ^{[2
]}

Wang, Changhong ^{[2
]}

机构：

[1] Nanchang Hangkong Univ, Coll Informat Engn, Nanchang 330063, Peoples R China

[2] Harbin Inst Technol, Space Control & Inertial Technol Res Ctr, Harbin 150001, Heilongjiang, Peoples R China

来源：

ABSTRACT AND APPLIED ANALYSIS | 2013年

基金：

中国博士后科学基金;

关键词：

EXISTENCE; SYNCHRONIZATION; EQUATIONS; CONSTANT; DYNAMICS;

D O I：

10.1155/2013/645262

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

This paper considers dynamical behaviors of a class of fuzzy impulsive reaction-diffusion delayed cellular neural networks (FIRDDCNNs) with time-varying periodic self-inhibitions, interconnection weights, and inputs. By using delay differential inequality, M-matrix theory, and analytic methods, some new sufficient conditions ensuring global exponential stability of the periodic FIRDDCNN model with Neumann boundary conditions are established, and the exponential convergence rate index is estimated. The differentiability of the time-varying delays is not needed. An example is presented to demonstrate the efficiency and effectiveness of the obtained results.

引用

页数：9

共 42 条

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