Centralized and decentralized data-sampling principles for outer-synchronization of fractional-order neural networks (Q2012817)
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scientific article; zbMATH DE number 6755989
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| English | Centralized and decentralized data-sampling principles for outer-synchronization of fractional-order neural networks |
scientific article; zbMATH DE number 6755989 |
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Centralized and decentralized data-sampling principles for outer-synchronization of fractional-order neural networks (English)
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3 August 2017
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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.
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data-sampling principles
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outer-synchronization
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fractional-order neural networks
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