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Neural Computing and Applications

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09.09.2019 | Original Article

Optimal quasi-synchronization of fractional-order memristive neural networks with PSOA

verfasst von: Lingzhong Zhang, Yongqing Yang

Erschienen in: Neural Computing and Applications | Ausgabe 13/2020

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Abstract

In this paper, optimal quasi-synchronization problem for fractional-order memristive delayed neural networks (FMDNNs) is investigated. The model of FMDNNs is transformed into systems with interval parameters. To guarantee quasi-synchronization, a general fractional-order inequalities and aperiodically intermittent controllers are proposed and analyzed. With the help of tools from interval matrix inequalities and fractional stability theory, sufficient conditions are obtained to guarantee quasi-synchronization of the FMDNNs. Synchronization errors about fractional order \(\alpha\) are clearly stated. The optimal control parameters satisfy the integral square error, and minimal control energy can be computed by using particle swarm optimization algorithm. Finally, simulation examples are given for illustration.

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Titel: Optimal quasi-synchronization of fractional-order memristive neural networks with PSOA
verfasst von: Lingzhong Zhang
Yongqing Yang
Publikationsdatum: 09.09.2019
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 13/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-019-04488-z

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