ABSOLUTE STABILITY CONDITIONS FOR DISCRETE-TIME RECURRENT NEURAL NETWORKS

被引：64
|
作者：
JIN, L
NIKIFORUK, PN
GUPTA, MM
机构：
[1] The authors are with the Intelligent Systems Research Laboratory, College of Engineering, University of Saskatchewan, Saskatoon, SK
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS | 1994年 / 5卷 / 06期
关键词：
D O I：
10.1109/72.329693
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, an analysis of the absolute stability for a general class of discrete-time recurrent neural networks (RNN's) is presented. A discrete-time model of RNN's is represented by a set of nonlinear difference equations. Some sufficient conditions for the absolute stability are derived using Ostrowski's theorem and the similarity transformation approach. For a given RNN model, these conditions are determined by the synaptic weight matrix of the network. The results reported in this paper need fewer constraints on the weight matrix and the model than in previously published studies.
引用
收藏
页码：954 / 964
页数：11
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