Centralized Data-Sampling Approach for GlobalOt-αSynchronization of Fractional-Order Neural Networks with Time Delays

In this paper, the globalO(t-α)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 theO(t-α)synchronization design strategy. A sufficient criterion is given under which the drive-response-based coupled neural networks can achieve globalO(t-α)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.