Globally asymptotical stability of discrete-time analog neural networks

被引：38

作者：

Jin, LA ^{[1
]}

Gupta, MM ^{[1
]}

机构：

[1] UNIV SASKATCHEWAN,COLL ENGN,INTELLIGENT SYST RES LAB,SASKATOON,SK S7N 5A9,CANADA

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 1996年 / 7卷 / 04期

关键词：

D O I：

10.1109/72.508944

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, some globally asymptotical stability criteria for the equilibrium states of a general class of discrete-time dynamic neural networks with continuous states are presented using a diagonal Lyapunov function approach, The neural networks are assumed to have the asymmetrical weight matrices throughout the paper. The resulting criteria are described by the diagonal stability of some matrices associated with the network parameters. Some novel stability conditions represented by either the existence of the positive diagonal solutions of the Lyapunov equations or some inequalities are given, Using the equivalence between the diagonal stability and the Schur stability for a nonnegative matrix, some simplified global stability conditions are also presented. Finally, some examples are provided for demonstrating the effectiveness of the global stability conditions presented.

引用

页码：1024 / 1031

页数：8

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