Delay-dependent multistability in recurrent neural networks

被引:60
作者
Huang, Gan [1 ,2 ]
Cao, Jinde [1 ]
机构
[1] Southeast Univ, Dept Math, Nanjing 210096, Peoples R China
[2] Shanghai Jiao Tong Univ, Sch Mech Engn, Shanghai 200240, Peoples R China
基金
高等学校博士学科点专项科研基金; 中国国家自然科学基金;
关键词
Multistability; Neural networks; Delay-dependent; Equilibria; Limit cycle; GLOBAL ROBUST STABILITY; ASYMPTOTIC STABILITY; ASSOCIATIVE MEMORY; STATE ESTIMATION; DISCRETE; SYNCHRONIZATION; BIFURCATION; CRITERION;
D O I
10.1016/j.neunet.2009.10.004
中图分类号
TP18 [人工智能理论];
学科分类号
140502 [人工智能];
摘要
In this article, we focus on the delay-dependent multistability in recurrent neural networks. By constructing Lyapunov functional and using matrix inequality techniques, a novel delay-dependent multistability criterion is derived. The obtained results are more flexible and less conservative than previously known criteria. Two examples are given to show the effectiveness of the obtained criteria. Furthermore, some interesting delay-dependent dynamic behaviors have been showed in a special case, for example, we find that there is the coexistence of stable equilibria and stable limit cycles in the single neuron. Also, when the neurons are coupled, then the stable patterns are more complex. (C) 2009 Elsevier Ltd. All rights reserved.
引用
收藏
页码:201 / 209
页数:9
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