Globally asymptotic stability of a class of neutral-type neural networks with delays

Chao Jung Cheng, Teh-Lu Liao, Jun Juh Yan, Chi-Chuan Hwang

Research output: Contribution to journalArticlepeer-review

71 Citations (Scopus)

Abstract

Several stability conditions for a class of systems with retarded-type delays are presented in the literature. However, no results have yet been presented for neural networks with neutral-type delays. Accordingly, this correspondence investigates the globally asymptotic stability of a class of neutral-type neural networks with delays. This class of systems includes Hopfield neural networks, cellular neural networks, and Cohen-Grossberg neural networks. Based on the Lyapunov stability method, two delay-independent sufficient stability conditions are derived. These stability conditions are easily checked and can be derived from the connection matrix and the network parameters without the requirement for any assumptions regarding the symmetry of the interconnections. Two illustrative examples are presented to demonstrate the validity of the proposed stability criteria.

Original languageEnglish
Pages (from-to)1191-1195
Number of pages5
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume36
Issue number5
DOIs
Publication statusPublished - 2006 Oct 1

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Artificial Intelligence
  • Human-Computer Interaction

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