TY - JOUR

T1 - Globally exponential stability condition of a class of neural networks with time-varying delays

AU - Liao, Teh Lu

AU - Yan, Jun Juh

AU - Cheng, Chao Jung

AU - Hwang, Chi Chuan

PY - 2005/5/23

Y1 - 2005/5/23

N2 - 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.

AB - 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.

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U2 - 10.1016/j.physleta.2005.03.034

DO - 10.1016/j.physleta.2005.03.034

M3 - Article

AN - SCOPUS:17844406403

VL - 339

SP - 333

EP - 342

JO - Physics Letters, Section A: General, Atomic and Solid State Physics

JF - Physics Letters, Section A: General, Atomic and Solid State Physics

SN - 0375-9601

IS - 3-5

ER -