An Extended Analysis on Robust Dissipativity of Uncertain Stochastic Generalized Neural Networks with Markovian Jumping Parameters
Usa Humphries,
Grienggrai Rajchakit,
Ramalingam Sriraman,
Pramet Kaewmesri,
Pharunyou Chanthorn,
Chee Peng Lim,
Rajendran Samidurai
Affiliations
Usa Humphries
Department of Mathematics, Faculty of Science, King Mongkut’s University of Technology Thonburi (KMUTT), Thung Khru 10140, Thailand
Grienggrai Rajchakit
Department of Mathematics, Faculty of Science, Maejo University, Chiang Mai 50290, Thailand
Ramalingam Sriraman
Department of Science and Humanities, Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Avadi, Tamil Nadu 600062, India
Pramet Kaewmesri
Department of Mathematics, Faculty of Science, King Mongkut’s University of Technology Thonburi (KMUTT), Thung Khru 10140, Thailand
Pharunyou Chanthorn
Research Center in Mathematics and Applied Mathematics, Department of Mathematics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand
Chee Peng Lim
Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, VIC 3216, Australia
Rajendran Samidurai
Department of Mathematics, Thiruvalluvar University, Vellore, Tamil Nadu 632115, India
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.
