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Discrete Dynamics in Nature and Society
Volume 2017 (2017), Article ID 6157292, 10 pages
https://doi.org/10.1155/2017/6157292
Research Article

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

Hubei Normal University, Hubei 435002, China

Correspondence should be addressed to Jin-E Zhang

Received 8 November 2016; Revised 18 December 2016; Accepted 9 January 2017; Published 9 February 2017

Academic Editor: Qasem M. Al-Mdallal

Copyright © 2017 Jin-E Zhang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.