A study on H∞ state estimation of static neural networks with time-varying delays

被引：77
作者：
Liu, Yajuan [1 ]
Lee, S. M. [1 ]
Kwon, O. M. [2 ]
Park, Ju H. [3 ]
机构：
[1] Daegu Univ, Dept Elect Engn, Gyongsan 712714, South Korea
[2] Chungbuk Natl Univ, Sch Elect Engn, Cheongju 361763, South Korea
[3] Yeungnam Univ, Dept Elect Engn, Kyongsan 712749, South Korea
来源：
APPLIED MATHEMATICS AND COMPUTATION | 2014年 / 226卷
关键词：
Static neural network; H-infinity Performance; State estimation; DEPENDENT STABILITY; DISTRIBUTED DELAYS; DISCRETE; DESIGN;
D O I：
10.1016/j.amc.2013.10.075
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
This paper studies the problem of H-infinity state estimation for static neural networks with time-varying delay. By construction of a suitable Lyapunov-Krasovskii functional, some improved delay-dependent conditions are established such that the error system is globally exponentially stable with a decay rate and a prescribed H-infinity performance is guaranteed. In order to get less conservative results of the state estimation condition, zero equalities and reciprocally convex approach are employed. The estimator gain matrix can be obtained in terms of the solution to linear matrix inequalities. Numerical examples are provided to illustrate the effectiveness and performance of the developed Method. (C) 2013 Elsevier Inc. All rights reserved.
引用
收藏
页码：589 / 597
页数：9
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