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

中国物理B ›› 2013, Vol. 22 ›› Issue (7): 78401-078401.doi: 10.1088/1674-1056/22/7/078401

• INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY • 上一篇下一篇

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

M. Kalpana, P. Balasubramaniam

Department of Mathematics, Gandhigram Rural Institute, Deemed University, Gandhigram 624 302, Tamilnadu, India

收稿日期:2012-12-17 修回日期:2012-12-24 出版日期:2013-06-01 发布日期:2013-06-01
基金资助:
Project supported by the Ministry of Science and Technology of India (Grant No. DST/Inspire Fellowship/2010/[293]/dt).

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

M. Kalpana, P. Balasubramaniam

Department of Mathematics, Gandhigram Rural Institute, Deemed University, Gandhigram 624 302, Tamilnadu, India

Received:2012-12-17 Revised:2012-12-24 Online:2013-06-01 Published:2013-06-01
Contact: P. Balasubramaniam E-mail:balugru@gmail.com
Supported by:
Project supported by the Ministry of Science and Technology of India (Grant No. DST/Inspire Fellowship/2010/[293]/dt).

摘要/Abstract

摘要： 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.

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

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.

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

中图分类号: (Neural networks)

84.35.+i

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

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[J]. 中国物理B, 2013, 22(7): 78401-078401.

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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

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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