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Global Stabilization of Memristive Neural Networks with Leakage and Time-Varying Delays Via Quantized Sliding-Mode Controller

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Abstract

This pape investigates the global stabilization of memristive neural networks (MNNs) with leakage and time-varying delays via quantized sliding-mode controller. The leakage delay is considered in the MNNs. Sliding mode controller is imported to ensure global stabilization of delayed MNNs. We also introduce two quantization schemes with uniform quantizer and logarithmic quantizer. Our goal is to deal with errors before and after quantization. We give some simulations and comparisons between two quantizers in the end of this paper.

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Funding

Funding was provided by National Natural Science Foundation of China (Grant No. 61673187).

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Authors and Affiliations

  1. College of Mathematics and Econometrics, Hunan University, Changsha, 410082, China

    Yuting Cao & Zhenyuan Guo

  2. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Bo Sun

  3. Science Program, Texas A&M University at Qatar, Doha, 23874, Qatar

    Tingwen Huang

  4. Australian AI Institute, University of Technology, Sydney, NSW, Australia

    Zheng Yan & Shiping Wen

Authors
  1. Yuting Cao
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  2. Bo Sun
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  3. Zhenyuan Guo
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  4. Tingwen Huang
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  5. Zheng Yan
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  6. Shiping Wen
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Corresponding author

Correspondence to Shiping Wen.

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Cao, Y., Sun, B., Guo, Z. et al. Global Stabilization of Memristive Neural Networks with Leakage and Time-Varying Delays Via Quantized Sliding-Mode Controller. Neural Process Lett 52, 2451–2468 (2020). https://doi.org/10.1007/s11063-020-10356-y

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