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Exponential synchronization of general chaotic delayed neural networks via hybrid feedback

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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.

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Authors and Affiliations

  1. School of Electrical Engineering, Zhejiang University, Hangzhou, 310027, China

    Mei-qin Liu & Jian-hai Zhang

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  1. Mei-qin Liu
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  2. Jian-hai Zhang
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Additional information

Project supported in part by the National Natural Science Foundation of China (No. 60504024), the Specialized Research Fund for the Doctoral Program of Higher Education, China (No. 20060335022), the Natural Science Foundation of Zhejiang Province (No. Y106010), China, and the ‘151 Talent Project” of Zhejiang Province (Nos. 05-3-1013 and 06-2-034), China

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Liu, Mq., Zhang, Jh. Exponential synchronization of general chaotic delayed neural networks via hybrid feedback. J. Zhejiang Univ. Sci. A 9, 262–270 (2008). https://doi.org/10.1631/jzus.A071336

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  • DOI: https://doi.org/10.1631/jzus.A071336

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