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Neural Processing Letters
Erschienen in: Neural Processing Letters 1/2020

19.05.2020

Quantized Control for Synchronization of Delayed Fractional-Order Memristive Neural Networks

verfasst von: Yingjie Fan, Xia Huang, Zhen Wang, Jianwei Xia, Hao Shen

Erschienen in: Neural Processing Letters | Ausgabe 1/2020

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Abstract

This research addresses the synchronization of delayed fractional-order memristive neural networks (DFMNNs) via quantized control. The motivations are twofold: (1) the transmitted information may be constrained by limited bandwidths; (2) the existing analysis techniques are difficult to establish LMI-based synchronization criteria for DFMNNs within a networked control environment. To overcome these difficulties, the logarithmic quantization is adopted to design two types of energy-saving and cost-effective quantized controllers. Then, under the framework of sector bound approach, the closed-loop drive-response DFMNNs can be represented as an interval system with uncertain feedback gains. By utilizing appropriate fractional-order Lyapunov functional and some inequality techniques, two LMI-based synchronization criteria for DFMNNs are derived to establish the relationship between the feedback gain and the quantization parameter. Finally, two illustrative examples are presented to validate the effectiveness of the proposed control schemes.
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Metadaten
Titel
Quantized Control for Synchronization of Delayed Fractional-Order Memristive Neural Networks
verfasst von
Yingjie Fan
Xia Huang
Zhen Wang
Jianwei Xia
Hao Shen
Publikationsdatum
19.05.2020
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 1/2020
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-020-10259-y

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