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Synchronization of master-slave memristive neural networks via fuzzy output-based adaptive strategy

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  • Alsaedi, Ahmed
  • Cao, Jinde
  • Ahmad, Bashir
  • Alshehri, Ahmed
  • Tan, Xuegang

Abstract

This paper considers the synchronization problem of memristive neural networks (MNNs) via a fuzzy output-based adaptive strategy, where the fuzzy model of MNNs with state-dependent memristor is employed. Several adaptive rules for the controller gain of the slave NNs and its connection weights are designed, which provide a new way to realize the state synchronization between master and slaver memristive NNs. Under these adaptive update rules, several synchronization results and their performance analysis are given, which verified by a simulation example.

Suggested Citation

  • Alsaedi, Ahmed & Cao, Jinde & Ahmad, Bashir & Alshehri, Ahmed & Tan, Xuegang, 2022. "Synchronization of master-slave memristive neural networks via fuzzy output-based adaptive strategy," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    • Handle: RePEc:eee:chsofr:v:158:y:2022:i:c:s0960077922003058
    DOI: 10.1016/j.chaos.2022.112095
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    References listed on IDEAS

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    Cited by:

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    2. Yang, Jinrong & Chen, Guici & Wen, Shiping & Wang, Leimin, 2023. "Finite-time dissipative control for discrete-time memristive neural networks via interval matrix method," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    3. Zhang, Lingzhong & Lu, Jianquan & Jiang, Bangxin & Huang, Chi, 2023. "Distributed synchronization of delayed dynamic networks under asynchronous delay-dependent impulsive control," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
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    5. Ramalingam Sriraman & Ohmin Kwon, 2024. "Global Exponential Synchronization of Delayed Quaternion-Valued Neural Networks via Decomposition and Non-Decomposition Methods and Its Application to Image Encryption," Mathematics, MDPI, vol. 12(21), pages 1-35, October.

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