A delay-partitioning projection approach to stability analysis of stochastic Markovian jump neural networks with randomly occurred nonlinearities

被引：16

作者：

Duan, Jianmin ^{[2
]}

Hu, Manfeng ^{[1
,2
]}

Yang, Yongqing ^{[1
,2
]}

Guo, Liuxiao ^{[2
]}

机构：

[1] Jiangnan Univ, Minist Educ, Key Lab Adv Proc Control Light Ind, Wuxi 214122, Peoples R China

[2] Jiangnan Univ, Sch Sci, Wuxi 214122, Peoples R China

来源：

NEUROCOMPUTING | 2014年 / 128卷

基金：

中国国家自然科学基金;

关键词：

Mean square asymptotic stability; Time-varying delay; Randomly occurred nonlinearities (RONs); Delay-partitioning projection; Stochastic neural networks; TIME-VARYING DELAY; DISTRIBUTED DELAYS; ASYMPTOTIC STABILITY; CONTINUOUS SYSTEMS; PARAMETERS; DISCRETE; SYNCHRONIZATION; COMPONENTS;

D O I：

10.1016/j.neucom.2013.08.019

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 considers the problem of mean square asymptotic stability of stochastic Markovian jump neural networks with randomly occurred nonlinearities. In terms of linear matrix inequality (LMI) approach and delay-partitioning projection technique, delay-dependent stability criteria are derived for the considered neural networks for cases with or without the information of the delay rates via new Lyapunov-Krasovskii functionals. We also establish that the conservatism of the conditions is a non-increasing function of the number of delay partitions. An example with simulation results is given to illustrate the effectiveness of the proposed approach. (C) 2013 Elsevier B.V. All rights reserved.

引用

页码：459 / 465

页数：7

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