STABILITY ANALYSIS FOR DELAYED NEURAL NETWORKS WITH DISCONTINUOUS NEURON ACTIVATIONS

被引：4

作者：

Guo, Zhenyuan ^{[1
]}

Huang, Lihong ^{[1
,2
]}

机构：

[1] Hunan Univ, Coll Math & Econometr, Changsha 410082, Hunan, Peoples R China

[2] Hunan Womens Univ, Dept Informat Technol, Changsha 410004, Hunan, Peoples R China

来源：

ASIAN JOURNAL OF CONTROL | 2013年 / 15卷 / 04期

关键词：

Delayed neural network; discontinuous neuron activation; global exponential stability; global convergence in measure; GLOBAL EXPONENTIAL STABILITY; TIME-VARYING DELAYS; DYNAMICAL BEHAVIORS; CONVERGENCE; SYSTEMS;

D O I：

10.1002/asjc.627

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper considers a class of delayed neural networks with discontinuous neuron activations. Based on the theory of differential equations with discontinuous right-hand sides, some novel sufficient conditions are derived that ensure the existence and global exponential stability of the equilibrium point. Moreover, by adopting the concept of convergence in measure, convergence behavior for the output is discussed. The obtained results are independent of the delay parameter and can be thought of as a generalization of previous results established for delayed neural networks with Lipschtz continuous neuron activations to the discontinuous case. Finally, we give a numerical example to illustrate the effectiveness and novelty of our results by comparing our results with those in the early literature.

引用

页码：1158 / 1167

页数：10