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Centralized Data-Sampling Approach for Global Synchronization of Fractional-Order Neural Networks with Time Delays

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  • Jin-E Zhang

Abstract

In this paper, the global synchronization problem is investigated for a class of fractional-order neural networks with time delays. Taking into account both better control performance and energy saving, we make the first attempt to introduce centralized data-sampling approach to characterize the synchronization design strategy. A sufficient criterion is given under which the drive-response-based coupled neural networks can achieve global synchronization. It is worth noting that, by using centralized data-sampling principle, fractional-order Lyapunov-like technique, and fractional-order Leibniz rule, the designed controller performs very well. Two numerical examples are presented to illustrate the efficiency of the proposed centralized data-sampling scheme.

Suggested Citation

  • Jin-E Zhang, 2017. "Centralized Data-Sampling Approach for Global Synchronization of Fractional-Order Neural Networks with Time Delays," Discrete Dynamics in Nature and Society, Hindawi, vol. 2017, pages 1-10, February.
    • Handle: RePEc:hin:jnddns:6157292
    DOI: 10.1155/2017/6157292
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