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

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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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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grants 61973199, 61573008 and Taishan Scholar Project of Shandong Province of China.

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Authors and Affiliations

  1. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, 266590, China

    Yingjie Fan & Xia Huang

  2. College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, 266590, China

    Zhen Wang

  3. College of Mathematic Science, Liaocheng University, Liaocheng, 252059, China

    Jianwei Xia

  4. School of Electrical Engineering and Information, Anhui University of Technology, Ma’anshan, 243002, China

    Hao Shen

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  1. Yingjie Fan
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  2. Xia Huang
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  3. Zhen Wang
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  4. Jianwei Xia
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  5. Hao Shen
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Correspondence to Xia Huang.

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Fan, Y., Huang, X., Wang, Z. et al. Quantized Control for Synchronization of Delayed Fractional-Order Memristive Neural Networks. Neural Process Lett 52, 403–419 (2020). https://doi.org/10.1007/s11063-020-10259-y

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