期刊
NEURAL NETWORKS
卷 22, 期 4, 页码 343-347出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2009.03.005
关键词
Delay-partitioning; Delay system; Linear matrix inequality (LMI); Stability; Static recurrent neural networks
资金
- [RGC HKU 7031/06P]
This paper introduces an effective approach to studying the stability of recurrent neural networks with a time-invariant delay. By employing a new Lyapumov-Krasovskii functional form based on delay partitioning, novel delay-dependent stability criteria are established to guarantee the global asymptotic stability of static neural networks. These conditions are expressed in the framework of linear matrix inequalities, which can be verified easily by means of standard software. It is shown, by comparing with existing approaches, that the delay-partitioning projection approach can largely reduce the conservatism of the stability results. Finally, two examples are given to show the effectiveness of the theoretical results. (C) 2009 Elsevier Ltd. All rights reserved.
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