Passive and exponential filter design for fuzzy neural networks

被引：46
作者：
Ahn, Choon Ki [1 ]
机构：
[1] Korea Univ, Sch Elect Engn, Seoul 136701, South Korea
来源：
INFORMATION SCIENCES | 2013年 / 238卷
关键词：
Passive filter; Exponential filter; Takagi-Sugeno fuzzy Hopfield neural networks; Linear matrix inequality (LMI); Lyapunov-Krasovskii stability theory; DEPENDENT STATE ESTIMATION; STABILITY ANALYSIS; H-INFINITY; STABILIZATION; SYSTEMS;
D O I：
10.1016/j.ins.2013.03.004
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper proposes a new passive and exponential filter for Takagi-Sugeno fuzzy Hopfield neural networks, with time delay and external disturbance. Based on the Lyapunov-Krasovskii stability theory, Jensen's inequality, and linear matrix inequality (LMI), a new delay-dependent criterion is proposed such that the filtering error system becomes exponentially stable and passive from the external disturbance to the output error. The proposed filter can be obtained by solving the LMI, which can be easily facilitated using standard numerical packages. Two numerical examples are given to illustrate the effectiveness of the proposed filter. (C) 2013 Elsevier Inc. All rights reserved.
引用
收藏
页码：126 / 137
页数：12
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