Centralized and Decentralized Data-Sampling Principles for Outer-Synchronization of Fractional-Order Neural Networks
Keyword(s):
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.
2021 ◽
2020 ◽
Vol 21
(6)
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pp. 571-587
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2011 ◽
Vol 193
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pp. 49-60
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2021 ◽
Keyword(s):
2019 ◽
Vol 38
(6)
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pp. 159-171
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