Centralized and decentralized data-sampling principles for outer-synchronization of fractional-order neural networks
type: Item
instance of: scholarly article
title: Centralized and decentralized data-sampling principles for outer-synchronization of fractional-order neural networks
zbMATH Open document ID: 1367.93356
DOI: 10.1155/2017/6290646
published in: Complexity
publication date: 2017-08-03T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 93C57
Mathematics Subject Classification ID: 92B20
Mathematics Subject Classification ID: 34A08
zbMATH DE Number: 6755989
zbMATH Keywords: data-sampling principles
zbMATH Keywords: outer-synchronization
zbMATH Keywords: fractional-order neural networks
author: Jin E. Zhang
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2017/6290646
OpenAlex ID: W2592953875
Wikidata QID: Q59142931
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DBLP publication ID: journals/complexity/Zhang17
description: scientific article; zbMATH DE number 6755989