Dissipativity analysis of memristive neural networks with time-varying delays and randomly occurring uncertainties

2015 ◽  
Vol 39 (11) ◽  
pp. 2896-2915 ◽  
Author(s):  
Ruoxia Li ◽  
Jinde Cao
Keyword(s):  
Neural Networks ◽  
Time Varying ◽  
Memristive Neural Networks ◽  
Randomly Occurring Uncertainties ◽  
Dissipativity Analysis

scholarly journals Dissipativity analysis of stochastic fuzzy neural networks with randomly occurring uncertainties using delay dividing approach

2019 ◽  
Vol 24 (4) ◽  
Author(s):  
Sundaram Senthilraj ◽  
Ramachandran Raja ◽  
Jinde Cao ◽  
Habib M. Fardoun
Keyword(s):  
Neural Networks ◽  
Sufficient Conditions ◽  
Lyapunov Stability Theory ◽  
Fuzzy Neural Networks ◽  
Linear Matrix ◽  
Time Varying ◽  
Randomly Occurring Uncertainties ◽  
Fuzzy Neural ◽  
Delay Partitioning ◽  
Dissipativity Analysis

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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