Linear matrix inequality approach for synchronization control of fuzzy cellular neural networks with mixed time delays

doi:10.1088/1674-1056/21/4/048402

中国物理B ›› 2012, Vol. 21 ›› Issue (4): 48402-048402.doi: 10.1088/1674-1056/21/4/048402

P. Balasubramaniam¹,M. Kalpana¹,R. Rakkiyappan²

收稿日期:2011-08-26 修回日期:2011-08-30 出版日期:2012-02-29 发布日期:2012-02-29
通讯作者: P. Balasubramaniam,balugru@gmail.com E-mail:balugru@gmail.com

Linear matrix inequality approach for synchronization control of fuzzy cellular neural networks with mixed time delays

P. Balasubramaniam^a)†, M. Kalpana^a), and R. Rakkiyappan^b)

a. Department of Mathematics, Gandhigram Rural Institute - Deemed University, Gandhigram-624 302, Tamilnadu, India;
b. Department of Mathematics, Bharathiar University, Coimbatore-641 046, Tamilnadu, India

Received:2011-08-26 Revised:2011-08-30 Online:2012-02-29 Published:2012-02-29
Contact: P. Balasubramaniam,balugru@gmail.com E-mail:balugru@gmail.com
Supported by:
Project supported by No. DST/INSPIRE Fellowship/2010/[293]/dt. 18/03/2011.

摘要/Abstract

Abstract: Fuzzy cellular neural networks (FCNNs) are special kinds of cellular neural networks (CNNs). Each cell in an FCNN contains fuzzy operating abilities. The entire network is governed by cellular computing laws. The design of FCNNs is based on fuzzy local rules. In this paper, a linear matrix inequality (LMI) approach for synchronization control of FCNNs with mixed delays is investigated. Mixed delays include discrete time-varying delays and unbounded distributed delays. A dynamic control scheme is proposed to achieve the synchronization between a drive network and a response network. By constructing the Lyapunov-Krasovskii functional which contains a triple-integral term and the free-weighting matrices method an improved delay-dependent stability criterion is derived in terms of LMIs. The controller can be easily obtained by solving the derived LMIs. A numerical example and its simulations are presented to illustrate the effectiveness of the proposed method.

Key words: asymptotic stability, chaos, fuzzy cellular neural networks, linear matrix inequalities, synchronization

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)

P. Balasubramaniam, M. Kalpana, R. Rakkiyappan. [J]. 中国物理B, 2012, 21(4): 48402-048402.

P. Balasubramaniam, M. Kalpana, and R. Rakkiyappan . Linear matrix inequality approach for synchronization control of fuzzy cellular neural networks with mixed time delays[J]. Chin. Phys. B, 2012, 21(4): 48402-048402.

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Linear matrix inequality approach for synchronization control of fuzzy cellular neural networks with mixed time delays

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