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Open AccessFeature PaperArticle

An Extended Analysis on Robust Dissipativity of Uncertain Stochastic Generalized Neural Networks with Markovian Jumping Parameters

1
Department of Mathematics, Faculty of Science, King Mongkut’s University of Technology Thonburi (KMUTT), Thung Khru 10140, Thailand
2
Department of Mathematics, Faculty of Science, Maejo University, Chiang Mai 50290, Thailand
3
Department of Science and Humanities, Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Avadi, Tamil Nadu 600062, India
4
Research Center in Mathematics and Applied Mathematics, Department of Mathematics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand
5
Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, VIC 3216, Australia
6
Department of Mathematics, Thiruvalluvar University, Vellore, Tamil Nadu 632115, India
*
Author to whom correspondence should be addressed.
Symmetry 2020, 12(6), 1035; https://doi.org/10.3390/sym12061035
Received: 15 May 2020 / Revised: 11 June 2020 / Accepted: 12 June 2020 / Published: 20 June 2020
(This article belongs to the Special Issue Symmetry in Nonlinear Studies)
The main focus of this research is on a comprehensive analysis of robust dissipativity issues pertaining to a class of uncertain stochastic generalized neural network (USGNN) models in the presence of time-varying delays and Markovian jumping parameters (MJPs). In real-world environments, most practical systems are subject to uncertainties. As a result, we take the norm-bounded parameter uncertainties, as well as stochastic disturbances into consideration in our study. To address the task, we formulate the appropriate Lyapunov–Krasovskii functional (LKF), and through the use of effective integral inequalities, simplified linear matrix inequality (LMI) based sufficient conditions are derived. We validate the feasible solutions through numerical examples using MATLAB software. The simulation results are analyzed and discussed, which positively indicate the feasibility and effectiveness of the obtained theoretical findings. View Full-Text
Keywords: neural networks; stochastic disturbance; robust dissipativity; Markovian jump parameters neural networks; stochastic disturbance; robust dissipativity; Markovian jump parameters
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Humphries, U.; Rajchakit, G.; Sriraman, R.; Kaewmesri, P.; Chanthorn, P.; Lim, C.P.; Samidurai, R. An Extended Analysis on Robust Dissipativity of Uncertain Stochastic Generalized Neural Networks with Markovian Jumping Parameters. Symmetry 2020, 12, 1035.

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