Title
An Extended Analysis on Robust Dissipativity of Uncertain Stochastic Generalized Neural Networks with Markovian Jumping Parameters.
Abstract
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.
Year
DOI
Venue
2020
10.3390/sym12061035
SYMMETRY-BASEL
Keywords
DocType
Volume
neural networks,stochastic disturbance,robust dissipativity,Markovian jump parameters
Journal
12
Issue
Citations 
PageRank 
6
1
0.36
References 
Authors
0
7
Name
Order
Citations
PageRank
Usa Humphries110.36
Grienggrai Rajchakit210011.87
Ramalingam Sriraman361.44
Pramet Kaewmesri410.36
Pharunyou Chanthorn571.12
Chee Peng Lim61459122.04
R. Samidurai727515.47