Nonlinear Measure Approach for the Stability Analysis of Complex-Valued Neural Networks

被引:22
作者
Gong, Weiqiang [1 ]
Liang, Jinling [1 ,2 ]
Zhang, Congjun [3 ]
Cao, Jinde [1 ,4 ]
机构
[1] Southeast Univ, Dept Math, Nanjing 210096, Jiangsu, Peoples R China
[2] King Abdulaziz Univ, Fac Engn, CSN Res Grp, Jeddah 21589, Saudi Arabia
[3] Nanjing Univ Finance & Econ, Coll Appl Math, Nanjing 210023, Jiangsu, Peoples R China
[4] King Abdulaziz Univ, Dept Math, Fac Sci, Jeddah 21589, Saudi Arabia
基金
中国国家自然科学基金;
关键词
Complex-valued neural networks; Nonlinear measure; Stability; Robust analysis; TIME-DELAYS; EXPONENTIAL STABILITY; ROBUST STABILITY; GLOBAL STABILITY; BIFURCATION; EXISTENCE; SYSTEMS; MODEL;
D O I
10.1007/s11063-015-9475-9
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Based on the nonlinear measure method and the matrix inequality techniques, this paper addresses the global asymptotic stability for the complex-valued neural networks with delay. Furthermore, robust stability of the addressed neural network with norm-bounded parameter uncertainties is also tackled. By constructing an appropriate Lyapunov functional candidate, several sufficient criteria are obtained to ascertain the existence, uniqueness and global stability of the equilibrium point of the addressed complex-valued neural networks, which are easy to be verified and implemented in practice. Finally, one example is given to illustrate the effectiveness of the obtained results.
引用
收藏
页码:539 / 554
页数:16
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