Globally exponential stability of generalized Cohen-Grossberg neural networks with delays

Chi Chuan Hwang, Chao Jung Cheng, Teh Lu Liao

Research output: Contribution to journalArticlepeer-review

112 Citations (Scopus)

Abstract

Based on the Halanay inequality lemma, this Letter derives a new sufficient condition for the globally exponential stability of the generalized Cohen-Grossberg neural networks with delays (GDCGNNs). The GDCGNN is quite general, and can describe several well-known neural networks with and without delays, including Hopfield and cellular neural networks. It is shown that the proposed sufficient condition relies on the connection matrices and the network parameters, and that it is independent of the delay parameter. Furthermore, the presented condition is easy to check, and is less restrictive than some of the sufficient conditions proposed in previous studies. The benefits of the developed sufficient condition are demonstrated by comparing its performance in a series of examples with that of several conditions presented previously.

Original languageEnglish
Pages (from-to)157-166
Number of pages10
JournalPhysics Letters, Section A: General, Atomic and Solid State Physics
Volume319
Issue number1-2
DOIs
Publication statusPublished - 2003 Dec 1

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)

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