Stochastic finite-time boundedness for Markovian jumping neural networks with time-varying delays

被引：54
|
作者：
Cheng, Jun [1 ]
Zhu, Hong [1 ]
Ding, Yucai [2 ]
Zhong, Shouming [3 ]
Zhong, Qishui [4 ]
机构：
[1] Univ Elect Sci & Technol China, Sch Automat Engn, Chengdu 611731, Sichuan, Peoples R China
[2] Southwest Univ Sci & Technol, Sch Sci, Mianyang 621010, Peoples R China
[3] Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Sichuan, Peoples R China
[4] Univ Elect Sci & Technol China, Sch Aeronaut & Astronaut, Chengdu 611731, Sichuan, Peoples R China
来源：
APPLIED MATHEMATICS AND COMPUTATION | 2014年 / 242卷
基金：
中国博士后科学基金;
关键词：
Finite-time boundedness; Neural networks; Markovian jump systems; Time delay; STABILITY ANALYSIS; STATE ESTIMATION; CONTROL-SYSTEMS; ROBUST; STABILIZATION; CRITERIA;
D O I：
10.1016/j.amc.2014.05.071
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper, a novel method is developed for the finite-time boundedness of Markovian jumping neural networks with time-varying delays. By introducing a newly augmented stochastic Lyapunov-Krasovskii functional and novel activation function conditions, sufficient condition for Markovian jumping neural networks is presented, and the state trajectory remains in a bounded region over a pre-specified finite-time interval. Finally, numerical examples are given to illustrate the efficiency and less conservative of the proposed method. (C) 2014 Elsevier Inc. All rights reserved.
引用
收藏
页码：281 / 295
页数：15
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