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

研究成果: Article同行評審

33 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)333-342
頁數10
期刊Physics Letters, Section A: General, Atomic and Solid State Physics
339
發行號3-5
DOIs
出版狀態Published - 2005 五月 23

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)

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