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

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• 作者：Liao ; Teh-Lu ; Yan ; Jun-Juh ; Cheng ; Chao-Jung ; Hwang ; Chi-Chuan
• 关键词：Exponential stability ; Hopfield neural networks ; Cellular neural networks ; Hamiltonian matrix
• 刊名：Physics Letters A
• 出版年：2005
• 出版时间：May 23, 2005
• 年：2005
• 卷：339
• 期：3-5
• 页码：333-342
• 全文大小：239 K

文摘

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.