Stability conditions for bi-directional associative memory neural networks with delay

被引：0
|
作者：
Zhou, DM [1 ]
Cao, JD [1 ]
机构：
[1] Yunnan Univ, Informat Coll, Kunming 650091, Peoples R China
来源：
PROCEEDINGS OF THE 3RD WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-5 | 2000年
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Bi-directional associative memory(BAM) models are two-layer heteroassociative networks. In the paper, the global asymptotic stability is studied for continuous bi-directional associative memory neural networks with axonal signal transmission delay while the neuronal output signal function S is not differentiable and strictly monotone increasing. Several sufficient conditions guaranteeing the network's global asymptotic stability are derived, the obtained results have important leading significance in the design and application of BAM.
引用
收藏
页码：809 / 812
页数：4
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