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Centralized and Decentralized Data-Sampling Principles for Outer-Synchronization of Fractional-Order Neural Networks

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  • Jin-E Zhang

Abstract

This paper aims to investigate the outer-synchronization of fractional-order neural networks. Using centralized and decentralized data-sampling principles and the theory of fractional differential equations, sufficient criteria about outer-synchronization of the controlled fractional-order neural networks are derived for structure-dependent centralized data-sampling, state-dependent centralized data-sampling, and state-dependent decentralized data-sampling, respectively. A numerical example is also given to illustrate the superiority of theoretical results.

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  • Jin-E Zhang, 2017. "Centralized and Decentralized Data-Sampling Principles for Outer-Synchronization of Fractional-Order Neural Networks," Complexity, Hindawi, vol. 2017, pages 1-11, March.
    • Handle: RePEc:hin:complx:6290646
    DOI: 10.1155/2017/6290646
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    1. Huang, Chengdai & Cao, Jinde & Xiao, Min & Alsaedi, Ahmed & Hayat, Tasawar, 2017. "Bifurcations in a delayed fractional complex-valued neural network," Applied Mathematics and Computation, Elsevier, vol. 292(C), pages 210-227.
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