Stability and Convergence Analysis for a Class of Neural Networks

被引：4

作者：

Gao, Xingbao ^{[1
]}

Liao, Li-Zhi ^{[2
]}

机构：

[1] Shaanxi Normal Univ, Coll Math & Informat Sci, Xian 710062, Peoples R China

[2] Hong Kong Baptist Univ, Dept Math, Kowloon, Hong Kong, Peoples R China

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2011年 / 22卷 / 11期

基金：

美国国家科学基金会;

关键词：

Convergence; exponential stability; neural network; variational inequality; VARIATIONAL-INEQUALITIES; OPTIMIZATION PROBLEMS; PROJECTION;

D O I：

10.1109/TNN.2011.2167760

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, we analyze and establish the stability and convergence of the dynamical system proposed by Xia and Feng, whose equilibria solve variational inequality and related problems. Under the pseudo-monotonicity and other conditions, this system is proved to be stable in the sense of Lyapunov and converges to one of its equilibrium points for any starting point. Meanwhile, the global exponential stability of this system is also shown under some mild conditions without the strong monotonicity of the mapping. The obtained results improve and correct some existing ones. The validity and performance of this system are demonstrated by some numerical examples.

引用

页码：1770 / 1782

页数：13

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