Dissipativity analysis for generalized neural networks with Markovian jump parameters and time-varying delay

2017 ◽  
Vol 89 (3) ◽  
pp. 2125-2140 ◽  
Author(s):  
Yanjun Shu ◽  
Xin-Ge Liu ◽  
Saibing Qiu ◽  
Fengxian Wang
Keyword(s):  
Neural Networks ◽  
Time Varying ◽  
Markovian Jump ◽  
Markovian Jump Parameters ◽  
Time Varying Delay ◽  
Generalized Neural Networks ◽  
Dissipativity Analysis ◽  
Varying Delay

Dissipativity analysis of delayed stochastic generalized neural networks with Markovian jump parameters

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Grienggrai Rajchakit ◽  
Ramalingam Sriraman ◽  
Rajendran Samidurai
Keyword(s):  
Linear Matrix ◽  
Time Varying ◽  
Matrix Inequalities ◽  
Markovian Jump ◽  
Stochastic Perturbations ◽  
Practical Applications ◽  
Markovian Jump Parameters ◽  
Generalized Neural Networks ◽  
System Information ◽  
Dissipativity Analysis

Abstract This article discusses the dissipativity analysis of stochastic generalized neural network (NN) models with Markovian jump parameters and time-varying delays. In practical applications, most of the systems are subject to stochastic perturbations. As such, this study takes a class of stochastic NN models into account. To undertake this problem, we first construct an appropriate Lyapunov–Krasovskii functional with more system information. Then, by employing effective integral inequalities, we derive several dissipativity and stability criteria in the form of linear matrix inequalities that can be checked by the MATLAB LMI toolbox. Finally, we also present numerical examples to validate the usefulness of the results.

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