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CLC number: TP183

On-line Access: 2008-01-10

Received: 2007-06-23

Revision Accepted: 2007-11-26

Crosschecked: 0000-00-00

Cited: 6

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Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE A 2008 Vol.9 No.2 P.262~270

http://doi.org/10.1631/jzus.A071336


Exponential synchronization of general chaotic delayed neural networks via hybrid feedback


Author(s):  Mei-qin LIU, Jian-hai ZHANG

Affiliation(s):  School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   liumeiqin@cee.zju.edu.cn, zjuzjh@gmail.com

Key Words:  Exponential synchronization, Hybrid feedback, Drive-response conception, Linear matrix inequality (LMI), Chaotic neural network model


Mei-qin LIU, Jian-hai ZHANG. Exponential synchronization of general chaotic delayed neural networks via hybrid feedback[J]. Journal of Zhejiang University Science A, 2008, 9(2): 262~270.

@article{title="Exponential synchronization of general chaotic delayed neural networks via hybrid feedback",
author="Mei-qin LIU, Jian-hai ZHANG",
journal="Journal of Zhejiang University Science A",
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pages="262~270",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A071336"
}

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%T Exponential synchronization of general chaotic delayed neural networks via hybrid feedback
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%A Jian-hai ZHANG
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%@ 1673-565X
%D 2008
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A071336

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T1 - Exponential synchronization of general chaotic delayed neural networks via hybrid feedback
A1 - Mei-qin LIU
A1 - Jian-hai ZHANG
J0 - Journal of Zhejiang University Science A
VL - 9
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EP - 270
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A071336


Abstract: 
This paper investigates the exponential synchronization problem of some chaotic delayed neural networks based on the proposed general neural network model, which is the interconnection of a linear delayed dynamic system and a bounded static nonlinear operator, and covers several well-known neural networks, such as Hopfield neural networks, cellular neural networks (CNNs), bidirectional associative memory (BAM) networks, recurrent multilayer perceptrons (RMLPs). By virtue of Lyapunov-Krasovskii stability theory and linear matrix inequality (LMI) technique, some exponential synchronization criteria are derived. Using the drive-response concept, hybrid feedback controllers are designed to synchronize two identical chaotic neural networks based on those synchronization criteria. Finally, detailed comparisons with existing results are made and numerical simulations are carried out to demonstrate the effectiveness of the established synchronization laws.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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