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Different Control Strategies for Fixed-Time Synchronization of Inertial Memristive Neural Networks

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Abstract

In this brief, fixed-time synchronization problem for inertial memristive neural networks (IMNNs) with impulsive and adaptive control is investigated. Instead of modeling the memristor as a right-hand discontinuous system, memristor is regarded as an uncertain continuous time-varying parameter, memristive neural networks (MNNs) is modeled as a neural network (NNs) with polytopic uncertainty and time varying parameters. By establishing comparison system, the criteria are established for synchronization of IMNNs in a setting time with impulsive and adaptive control input. Based on convex combination method, the influence of different impulsive effects on synchronization behavior of the system is analyzed by dividing the impulsive interval. Finally, numerical examples are given for illustration.

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Authors and Affiliations

  1. School of Electrical Engineering and Automation, Changshu Institute of Technology, Changshu, People’s Republic of China

    Lingzhong Zhang

  2. Wuxi Engineering Research Center for Biocomputing, School of Science, Jiangnan University, Wuxi, 214122, People’s Republic of China

    Yongqing Yang

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  1. Lingzhong Zhang
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  2. Yongqing Yang
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Correspondence to Yongqing Yang.

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This work was jointly supported by the National Science Research Project of Colleges and Universities in Jiangsu No.17KJB510002, the China Postdoctoral Science Foundation No. 2020M672027.

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Zhang, L., Yang, Y. Different Control Strategies for Fixed-Time Synchronization of Inertial Memristive Neural Networks. Neural Process Lett 54, 3657–3678 (2022). https://doi.org/10.1007/s11063-022-10779-9

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