Less conservative delay-dependent stability criteria for neural networks with time-varying delays

被引:13
作者
Shao, Hanyong [1 ]
机构
[1] Qufu Normal Univ, Sch Elect & Informat Automat, Rizhao 276826, Shandong, Peoples R China
关键词
Neural networks; Static neural networks; Local field neural networks; Lyapunov functional; Globally asymptotically stable; GLOBAL EXPONENTIAL STABILITY; ASYMPTOTIC STABILITY; ROBUST STABILITY; DISTRIBUTED DELAYS; PERIODICITY; SYSTEMS;
D O I
10.1016/j.neucom.2010.01.006
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper is concerned with the stability for static neural networks with time-varying delays. With an appropriate Lyapunov functional formulated, a new technique is proposed to up bound the derivative of the Lyapunov functional. A delay-dependent stability criterion is obtained by proving the bound negative definite with convex combination methods. The delay-dependent stability criterion is simpler and less conservative than some existing ones. Both delay-independent and delay-dependent criteria are obtained, which can be checked easily using the recently developed algorithms. Examples are provided to illustrate the effectiveness and the reduced conservatism of the proposed results. (C) 2010 Elsevier B.V. All rights reserved.
引用
收藏
页码:1528 / 1532
页数:5
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