Stability and synchronization for complex-valued neural networks with stochastic parameters and mixed time delays

被引：6
作者：
Liu, Yufei [1 ,2 ]
Shen, Bo [1 ,2 ]
Sun, Jie [1 ,2 ]
机构：
[1] Donghua Univ, Coll Informat Sci & Technol, Shanghai 201620, Peoples R China
[2] Engn Res Ctr Digitalized Textile & Fash Technol, Minist Educ, Shanghai 201620, Peoples R China
来源：
COGNITIVE NEURODYNAMICS | 2023年 / 17卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Complex-valued neural networks; Mixed time delays; Stochastic parameters; Stability; Synchronization; GLOBAL ASYMPTOTIC STABILITY; SYSTEMS; LEAKAGE;
D O I：
10.1007/s11571-022-09823-0
中图分类号：
Q189 [神经科学];
学科分类号：
071006 ;
摘要：
In this paper, a class of complex-valued neural networks (CVNNs) with stochastic parameters and mixed time delays are proposed. The random fluctuation of system parameters is considered in order to describe the implementation of CVNNs more practically. Mixed time delays including distributed delays and time-varying delays are also taken into account in order to reflect the influence of network loads and communication constraints. Firstly, the stability problem is investigated for the CVNNs. In virtue of Lyapunov stability theory, a sufficient condition is deduced to ensure that CVNNs are asymptotically stable in the mean square. Then, for an array of coupled identical CVNNs with stochastic parameters and mixed time delays, synchronization issue is investigated. A set of matrix inequalities are obtained by using Lyapunov stability theory and Kronecker product and if these matrix inequalities are feasible, the addressed CVNNs are synchronized. Finally, the effectiveness of the obtained theoretical results is demonstrated by two numerical examples.
引用
收藏
页码：1213 / 1227
页数：15
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