Globally exponential stability condition of a class of neural networks with time-varying delays

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

Research output: Contribution to journalArticle

33 Citations (Scopus)

Abstract

In this Letter, the globally exponential stability for a class of neural networks including Hopfield neural networks and cellular neural networks with time-varying delays is investigated. Based on the Lyapunov stability method, a novel and less conservative exponential stability condition is derived. The condition is delay-dependent and easily applied only by checking the Hamiltonian matrix with no eigenvalues on the imaginary axis instead of directly solving an algebraic Riccati equation. Furthermore, the exponential stability degree is more easily assigned than those reported in the literature. Some examples are given to demonstrate validity and excellence of the presented stability condition herein.

Original languageEnglish
Pages (from-to)333-342
Number of pages10
JournalPhysics Letters, Section A: General, Atomic and Solid State Physics
Volume339
Issue number3-5
DOIs
Publication statusPublished - 2005 May 23

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)

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