Sampled-data synchronization of randomly coupled reaction–diffusion neural networks with Markovian jumping and mixed delays using multiple integral approach

2015 ◽  
Vol 28 (3) ◽  
pp. 449-462 ◽  
Author(s):  
R. Rakkiyappan ◽  
S. Dharani
Keyword(s):  
Neural Networks ◽  
Reaction Diffusion ◽  
Multiple Integral ◽  
Data Synchronization ◽  
Integral Approach ◽  
Mixed Delays ◽  
Sampled Data ◽  
Markovian Jumping ◽  
Coupled Reaction

Stochastic stability of fuzzy Markovian jump neural networks by multiple integral approach

2018 ◽  
Vol 11 (1) ◽  
pp. 81-105
Author(s):  
Cheng-De Zheng
Keyword(s):  
Neural Networks ◽  
Distributed Delay ◽  
Multiple Integral ◽  
Integral Approach ◽  
Time Varying ◽  
Markovian Jumping ◽  
Content Type ◽  
Continuously Distributed Delay ◽  
Time Varying Delay ◽  
Varying Delay

Purpose The purpose of this paper is to develop a methodology for the stochastically asymptotic stability of fuzzy Markovian jumping neural networks with time-varying delay and continuously distributed delay in mean square. Design/methodology/approach The authors perform Briat Lemma, multiple integral approach and linear convex combination technique to investigate a class of fuzzy Markovian jumping neural networks with time-varying delay and continuously distributed delay. New sufficient criterion is established by linear matrix inequalities conditions. Findings It turns out that the obtained methods are easy to be verified and result in less conservative conditions than the existing literature. Two examples show the effectiveness of the proposed results. Originality/value The novelty of the proposed approach lies in establishing a new Wirtinger-based integral inequality and the use of the Lyapunov functional method, Briat Lemma, multiple integral approach and linear convex combination technique for stochastically asymptotic stability of fuzzy Markovian jumping neural networks with time-varying delay and continuously distributed delay in mean square.

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