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

Jin-E Zhang

Complexity, 2017, vol. 2017, 1-11

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.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:6290646

DOI: 10.1155/2017/6290646

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