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

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

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 pract...

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Bibliographic Details
Main Authors: Usa Humphries, Grienggrai Rajchakit, Ramalingam Sriraman, Pramet Kaewmesri, Pharunyou Chanthorn, Chee Peng Lim, Rajendran Samidurai
Format: Article
Language:English
Published: MDPI AG 2020-06-01
Series:Symmetry
Subjects:
neural networks
stochastic disturbance
robust dissipativity
Markovian jump parameters
Online Access:https://www.mdpi.com/2073-8994/12/6/1035

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