Stability and Dissipativity Analysis for Neutral Type Stochastic Markovian Jump Static Neural Networks with Time Delays-Reference-Cited by-同舟云学术

Stability and Dissipativity Analysis for Neutral Type Stochastic Markovian Jump Static Neural Networks with Time Delays

Published:2019-05-09 Issue:3 Volume:9 Page:189-204
ISSN:2083-2567
Container-title:Journal of Artificial Intelligence and Soft Computing Research
language:en
Short-container-title:

Author:

Cao Yang¹,Samidurai R.²,Sriraman R.²

Affiliation:

1. Department of Mechanical Engineering , The University of Hong Kong , Pokfulam , Hong Kong

2. Department of Mathematics , Thiruvalluvar University , Vellore 631 115 , India

Abstract

Abstract This paper studies the global asymptotic stability and dissipativity problem for a class of neutral type stochastic Markovian Jump Static Neural Networks (NTSMJSNNs) with time-varying delays. By constructing an appropriate Lyapunov-Krasovskii Functional (LKF) with some augmented delay-dependent terms and by using integral inequalities to bound the derivative of the integral terms, some new sufficient conditions have been obtained, which ensure that the global asymptotic stability in the mean square. The results obtained in this paper are expressed in terms of Strict Linear Matrix Inequalities (LMIs), whose feasible solutions can be verified by effective MATLAB LMI control toolbox. Finally, examples and simulations are given to show the validity and advantages of the proposed results.

Publisher

Walter de Gruyter GmbH

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Hardware and Architecture,Modelling and Simulation,Information Systems

Link

https://www.sciendo.com/pdf/10.2478/jaiscr-2019-0003

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