Asymptotical Synchronization for Delayed Stochastic Neural Networks with Uncertainty via Adaptive Control

被引：45
|
作者：
Tong, Dongbing [1 ,2 ]
Zhang, Liping [1 ]
Zhou, Wuneng [3 ]
Zhou, Jun [3 ]
Xu, Yuhua [4 ]
机构：
[1] Shanghai Univ Engn Sci, Sch Elect & Elect Engn, Shanghai 201620, Peoples R China
[2] Fudan Univ, Sch Informat Sci & Technol, Dept Elect Engn, Shanghai 200433, Peoples R China
[3] Donghua Univ, Coll Informat Sci & Technol, Shanghai 200051, Peoples R China
[4] Nanjing Audit Univ, Sch Finance, Nanjing 211815, Jiangsu, Peoples R China
来源：
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS | 2016年 / 14卷 / 03期
基金：
中国国家自然科学基金; 上海市自然科学基金;
关键词：
Neural networks; stochastic noises; synchronization control; time-delays; uncertainty; COMPLEX DYNAMICAL NETWORKS; STATE ESTIMATION; EXPONENTIAL SYNCHRONIZATION; DISTRIBUTED DELAYS; ROBUST STABILITY; SYSTEMS; DISCRETE; MODEL;
D O I：
10.1007/s12555-015-0077-0
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, the problem of the adaptive synchronization control is considered for neural networks with uncertainty and stochastic noise. Via utilizing stochastic analysis method and linear matrix inequality (LMI) approach, several sufficient conditions to ensure the adaptive synchronization for neural networks are derived. By the adaptive feedback methods, some suitable parameters update laws are found. Finally, a simulation result is provided to substantiate the effectiveness of the proposed approach.
引用
收藏
页码：706 / 712
页数：7
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