Global Lagrange Stability for Takagi-Sugeno Fuzzy Cohen-Grossberg BAM Neural Networks with Time-varying Delays

被引：12
|
作者：
Wang, Jingfeng [1 ]
Tian, Lixin [2 ]
Zhen, Zaili [1 ]
机构：
[1] Jiangsu Univ, Sch Fac Sci, 301 Xuefu Rd, Zhenjiang 212013, Peoples R China
[2] Nanjing Normal Univ, Sch Math Sci, 1 Yuen Rd, Nanjing 210046, Jiangsu, Peoples R China
来源：
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS | 2018年 / 16卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Cohen-Grossberg BAM neural networks; Lagrange stability; Lyapunov functional; time-varying delays; T-S fuzzy model; EXPONENTIAL STABILITY; NEUTRAL-TYPE; ASYMPTOTIC STABILITY; PERIODIC-SOLUTION; SYNCHRONIZATION; STABILIZATION; SYSTEMS; EXISTENCE; DISCRETE; DESIGN;
D O I：
10.1007/s12555-017-0618-9
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 concerns the globally exponential stability in Lagrange sense for Takagi-Sugeno (T-S) fuzzy Cohen-Grossberg BAM neural networks with time-varying delays. Based on the Lyapunov functional method and inequality techniques, two different types of activation functions which include both Lipschitz function and general activation functions are analyzed. Several sufficient conditions in linear matrix inequality form are derived to guarantee the Lagrange exponential stability of Cohen-Grossberg BAM neural networks with time-varying delays which are represented by T-S fuzzy models. Finally, simulation results demonstrate the effectiveness of the theoretical results.
引用
收藏
页码：1603 / 1614
页数：12
相关论文
共 50 条
12345