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Centralized and Decentralized Data-Sampling Principles for Outer-Synchronization of Fractional-Order Neural Networks
Author
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-03-08
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Jin-E, Zhang. 2017. Centralized and Decentralized Data-Sampling Principles for Outer-Synchronization of Fractional-Order Neural Networks. Complexity،Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1143237
Modern Language Association (MLA)
Jin-E, Zhang. Centralized and Decentralized Data-Sampling Principles for Outer-Synchronization of Fractional-Order Neural Networks. Complexity No. 2017 (2017), pp.1-11.
https://search.emarefa.net/detail/BIM-1143237
American Medical Association (AMA)
Jin-E, Zhang. Centralized and Decentralized Data-Sampling Principles for Outer-Synchronization of Fractional-Order Neural Networks. Complexity. 2017. Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1143237
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1143237