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

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.

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