Dissipativity analysis of delayed stochastic generalized neural networks with Markovian jump parameters-Reference-Cited by-同舟云学术

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

Published:2021-03-11 Issue:0 Volume:0 Page:
ISSN:2191-0294
Container-title:International Journal of Nonlinear Sciences and Numerical Simulation
language:en
Short-container-title:

Author:

Rajchakit Grienggrai¹^ORCID,Sriraman Ramalingam²,Samidurai Rajendran²

Affiliation:

1. Department of Mathematics, Thiruvalluvar University , Vellore, Tamil Nadu , 632115 , India

2. Department of Mathematics , Thiruvalluvar University , Vellore , Tamil Nadu , 632115 , India

Abstract

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.

Publisher

Walter de Gruyter GmbH

Subject

Applied Mathematics,General Physics and Astronomy,Mechanics of Materials,Engineering (miscellaneous),Modeling and Simulation,Computational Mechanics,Statistical and Nonlinear Physics

Link

https://www.degruyter.com/document/doi/10.1515/ijnsns-2019-0244/pdf

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