Dissipativity Analysis for Neural Networks With Time-Varying Delays via a Delay-Product-Type Lyapunov Functional Approach

2020 ◽  
pp. 1-10 ◽  
Author(s):  
Hong-Hai Lian ◽  
Shen-Ping Xiao ◽  
Huaicheng Yan ◽  
Fuwen Yang ◽  
Hong-Bing Zeng
Keyword(s):  
Neural Networks ◽  
Lyapunov Functional ◽  
Functional Approach ◽  
Product Type ◽  
Time Varying ◽  
Dissipativity Analysis

Delay-Dependent Asymptotical Stabilization Criterion of Recurrent Neural Networks

2013 ◽  
Vol 330 ◽  
pp. 1045-1048 ◽  
Author(s):  
Grienggrai Rajchakit
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Lyapunov Functional ◽  
Functional Approach ◽  
Linear Matrix ◽  
Time Varying ◽  
Matrix Inequality ◽  
Weighting Matrix ◽  
Delay Dependent ◽  
Delay Dependent Stability

This paper deals with the problem of delay-dependent stability criterion of discrete-time recurrent neural networks with time-varying delays. Based on quadratic Lyapunov functional approach and free-weighting matrix approach, some linear matrix inequality criteria are found to guarantee delay-dependent asymptotical stability of these systems. And one example illustrates the exactness of the proposed criteria.

Lyapunov Functional Approach to Stability Analysis of Riemann-Liouville Fractional Neural Networks with Time-Varying Delays

2017 ◽  
Vol 20 (5) ◽  
pp. 1938-1951 ◽  
Author(s):  
Hai Zhang ◽  
Renyu Ye ◽  
Jinde Cao ◽  
Alsaedi Ahmed ◽  
Xiaodi Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Lyapunov Functional ◽  
Functional Approach ◽  
Time Varying

Exponential stability and periodicity of memristor-based recurrent neural networks with time-varying delays

2017 ◽  
Vol 10 (02) ◽  
pp. 1750027 ◽  
Author(s):  
Wei Zhang ◽  
Chuandong Li ◽  
Tingwen Huang
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Sufficient Conditions ◽  
Functional Approach ◽  
Linear Matrix ◽  
Time Varying ◽  
Matrix Inequality ◽  
Matrix Inequalities ◽  
Inclusion Theory ◽  
The Stability

In this paper, the stability and periodicity of memristor-based neural networks with time-varying delays are studied. Based on linear matrix inequalities, differential inclusion theory and by constructing proper Lyapunov functional approach and using linear matrix inequality, some sufficient conditions are obtained for the global exponential stability and periodic solutions of memristor-based neural networks. Finally, two illustrative examples are given to demonstrate the results.

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