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Complexity
Volume 2017, Article ID 6290646, 11 pages
https://doi.org/10.1155/2017/6290646
Research Article

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

Hubei Normal University, Hubei 435002, China

Correspondence should be addressed to Jin-E Zhang; moc.361@50212068gnahz

Received 24 December 2016; Revised 6 February 2017; Accepted 21 February 2017; Published 8 March 2017

Academic Editor: Olfa Boubaker

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

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.