Exponential stabilization of delayed recurrent neural networks: A state estimation based approach

被引：56

作者：

Huang, He ^{[1
]}

Huang, Tingwen ^{[2
]}

Chen, Xiaoping ^{[1
]}

Qian, Chunjiang ^{[3
]}

机构：

[1] Soochow Univ, Sch Elect & Informat Engn, Suzhou 215006, Peoples R China

[2] Texas A&M Univ, Doha 5825, Qatar

[3] Univ Texas San Antonio, Dept Elect & Comp Engn, San Antonio, TX 78249 USA

来源：

NEURAL NETWORKS | 2013年 / 48卷

基金：

中国国家自然科学基金;

关键词：

Recurrent neural networks; Time delay; Exponential stabilization; State estimation; Decoupling; TIME-VARYING DELAYS; GLOBAL ASYMPTOTIC STABILITY; DISTRIBUTED DELAYS; DISCRETE; PARAMETERS;

D O I：

10.1016/j.neunet.2013.08.006

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 stabilization problem of delayed recurrent neural networks. As the states of neurons are usually difficult to be fully measured, a state estimation based approach is presented. First, a sufficient condition is derived such that the augmented system under consideration is globally exponentially stable. Then, by employing a decoupling technique, the gain matrices of the controller and state estimator are achieved by solving some linear matrix inequalities. Finally, a delayed neural network with chaotic behaviors is exploited to demonstrate the applicability of the developed result. (C) 2013 Elsevier Ltd. All rights reserved.

引用

页码：153 / 157

页数：5

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