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Fixed-Time Control for Memristor-Based Quaternion-Valued Neural Networks with Discontinuous Activation Functions

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Abstract

This paper studies the fixed-time synchronization control of quaternion-valued memristive neural networks (QVMNNs). The QVMNN is the extension of real and complex-valued MNNs, and the activation functions considered in this work are assumed to be discontinuous. Due to the noncommutativity of quaternion multiplication, the QVMNNs model is separated into four real-valued systems by utilizing the differential inclusion theory and decomposition method. Based on the sign function, some discontinuous control schemes are developed. By applying the nonsmooth analysis and inequality techniques, some novel criteria for fixed-time synchronization of QVMNNs are derived. Compared with the previous results, the proposed method based on sign function makes the designed controllers more concise and the established criteria more effective and less conservative. Finally, simulations are proposed to demonstrate the validity and practicability of theoretical results.

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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Funding

This research was supported by the Natural Science Foundation of Jiangsu Province under Grant No. BK20210635, Key Project of Natural Science Foundation of China (No. 61833005).

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Authors and Affiliations

  1. School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Ruoyu Wei

  2. School of Mathematics, and Research Center for Complex Systems and Network Sciences, Southeast University, Nanjing, 210096, China

    Jinde Cao

  3. Department of Innovative Technologies, National Research Tomsk State University, Tomsk, 634050, Russia

    Sergey Gorbachev

Authors
  1. Ruoyu Wei
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  2. Jinde Cao
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  3. Sergey Gorbachev
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Correspondence to Ruoyu Wei.

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Wei, R., Cao, J. & Gorbachev, S. Fixed-Time Control for Memristor-Based Quaternion-Valued Neural Networks with Discontinuous Activation Functions. Cogn Comput 15, 50–60 (2023). https://doi.org/10.1007/s12559-022-10057-9

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  • DOI: https://doi.org/10.1007/s12559-022-10057-9

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