Stochastic asymptotical synchronization of chaotic Markovian jumping fuzzy cellular neural networks with mixed delays and Wiener process based on sampled-data control

doi:10.1088/1674-1056/22/7/078401

Abstract We investigate the stochastic asymptotical synchronization of chaotic Markovian jumping fuzzy cellular neural networks (MJFCNNs) with discrete unbounded distributed delays, and Wiener process based on sampled-data control using linear matrix inequality (LMI) approach. Lyapunov-Krasovskii functional (LKF) combining with the input delay approach as well as the free-weighting matrix approach is employed to derive several sufficient criteria in terms of LMIs to ensure the delayed MJFCNNs with the Wiener process is stochastic asymptotical synchronous. Restrictions (e.g., time derivative is smaller than one) are removed to obtain a proposed sampled-data controller. Finally, a numerical example is provided to demonstrate the reliability of the derived results.

Keywords: stochastic asymptotical synchronization fuzzy cellular neural networks chaotic Markovian jumping parameters sampled-data control

Received: 17 December 2012
Revised: 24 December 2012
Accepted manuscript online:

PACS:	84.35.+i	(Neural networks)
	05.45.Gg	(Control of chaos, applications of chaos)
	05.45.Xt	(Synchronization; coupled oscillators)
	07.05.Mh	(Neural networks, fuzzy logic, artificial intelligence)

Fund: Project supported by the Ministry of Science and Technology of India (Grant No. DST/Inspire Fellowship/2010/[293]/dt).

Corresponding Authors: P. Balasubramaniam
E-mail: balugru@gmail.com

Cite this article:

M. Kalpana, P. Balasubramaniam Stochastic asymptotical synchronization of chaotic Markovian jumping fuzzy cellular neural networks with mixed delays and Wiener process based on sampled-data control 2013 Chin. Phys. B 22 078401

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Stochastic asymptotical synchronization of chaotic Markovian jumping fuzzy cellular neural networks with mixed delays and Wiener process based on sampled-data control

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