Multiple ψ-Type Stability and Its Robustness for Recurrent Neural Networks With Time-Varying Delays

被引：26

作者：

Zhang, Fanghai ^{[1
]}

Zeng, Zhigang ^{[1
]}

机构：

[1] Huazhong Univ Sci & Technol, Guangdong Prov Key Lab Digital Mfg Equipment, Key Lab Image Proc & Intelligent Control Educ,Sch, Guangdong HUST Ind Technol Res Inst,Minist China, Wuhan 430074, Hubei, Peoples R China

来源：

IEEE TRANSACTIONS ON CYBERNETICS | 2019年 / 49卷 / 05期

关键词：

Multiple psi-type stability; recurrent neural networks (RNNs); robustness; time-varying delays; GLOBAL EXPONENTIAL STABILITY; LINEAR ACTIVATION FUNCTIONS; ASYMPTOTIC STABILITY; LAGRANGE STABILITY; MU-STABILITY; FINITE-TIME; MULTISTABILITY; CONVERGENCE; SYNCHRONIZATION; SYSTEMS;

D O I：

10.1109/TCYB.2018.2813979

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 psi-type stability and robustness of recurrent neural networks are investigated by using the differential inequality. By utilizing psi-type functions combined with the inequality techniques, some sufficient conditions ensuring psi-type stability and robustness are derived for linear neural networks with time-varying delays. Then, by choosing appropriate Lipschitz coefficient in subregion, some algebraic criteria of the multiple psi-type stability and robust boundedness are established for the delayed neural networks with time-varying delays. For special cases, several criteria are also presented by selecting parameters with easy implementation. The derived results cover both psi-type mono-stability and multiple psi-type stability. In addition, these theoretical results contain exponential stability, polynomial stability, and mu-stability, and they also complement and extend some previous results. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed criteria.

引用

页码：1803 / 1815

页数：13

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