Dissipativity analysis of stochastic fuzzy neural networks with randomly occurring uncertainties using delay dividing approach
Articles
Sundaram Senthilraj
Velalar College of Engineering & Technology
Ramachandran Raja
Alagappa University
Jinde Cao
Southeast University
https://orcid.org/0000-0003-3133-7119
Habib M. Fardoun
King Abdulaziz University
Published 2019-06-27
https://doi.org/10.15388/NA.2019.4.5
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Keywords

dissipativity
stochastic fuzzy neural network
time-varying delay

How to Cite

Senthilraj S., Raja R., Cao J. and Fardoun H. M. (2019) “Dissipativity analysis of stochastic fuzzy neural networks with randomly occurring uncertainties using delay dividing approach”, Nonlinear Analysis: Modelling and Control, 24(4), pp. 561–581. doi: 10.15388/NA.2019.4.5.

Abstract

This paper focuses on the problem of delay-dependent robust dissipativity analysis for a class of stochastic fuzzy neural networks with time-varying delay. The randomly occurring uncertainties under consideration are assumed to follow certain mutually uncorrelated Bernoulli-distributed white noise sequences. Based on the Itô's differential formula, Lyapunov stability theory, and linear matrix inequalities techniques, several novel sufficient conditions are derived using delay partitioning approach to ensure the dissipativity of neural networks with or without time-varying parametric uncertainties. It is shown, by comparing with existing approaches, that the delay-partitioning projection approach can largely reduce the conservatism of the stability results. Numerical examples are constructed to show the effectiveness of the theoretical results.

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