Global Exponential Stability of a General Class of Recurrent Neural Networks with Variable Delays

被引：0

作者：

Guo Z. ^{[1
]}

Huang L. ^{[1
]}

机构：

[1] College of Mathematics and Econometrics, Hunan University

来源：

Differential Equations and Dynamical Systems | 2011年 / 19卷 / 1-2期

基金：

中国国家自然科学基金;

关键词：

Global exponential stability; Lyapunov functional; Neural networks; Topological degree;

D O I：

10.1007/s12591-011-0082-6

中图分类号：

学科分类号：

摘要：

Without assuming boundedness, differentiability, and monotonicity of the activation functions, new conditions ensuring the existence, uniqueness, and global exponential stability of the equilibrium point of a class of recurrent neural networks with variable delays are derived by utilizing the theory of topological degree and constructing appropriate Lyapunov functionals. Some stability results from previous works are extended and improved. © 2011 Foundation for Scientific Research and Technological Innovation.

引用

页码：133 / 148

页数：15

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