Synchronization of inertial complex-valued memristor-based neural networks with time-varying delays

Pan Wang; Xuechen Li; Qianqian Zheng; Pan Wang; Xuechen Li; Qianqian Zheng

doi:10.3934/mbe.2024147

Mathematical Biosciences and Engineering

2024, Volume 21, Issue 2: 3319-3334. doi: 10.3934/mbe.2024147

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Synchronization of inertial complex-valued memristor-based neural networks with time-varying delays

School of Science, Xuchang University, Xuchang 461000, China

Academic Editor: Regino Criado Herrero

Received: 19 December 2023 Revised: 17 January 2024 Accepted: 20 January 2024 Published: 04 February 2024

The synchronization of inertial complex-valued memristor-based neural networks (ICVMNNs) with time-varying delays was explored in the paper with the non-separation and non-reduced approach. Sufficient conditions required for the exponential synchronization of the ICVMNNs were identified with the construction of comprehensive Lyapunov functions and the design of a novel control scheme. The adaptive synchronization was also investigated based on the derived results, which is easier to implement in practice. What's more, a numerical example that verifies the obtained results was presented.
- inertial complex-valued neural networks,
- exponential synchronization,
- adaptive synchronization,
- memristor
Citation: Pan Wang, Xuechen Li, Qianqian Zheng. Synchronization of inertial complex-valued memristor-based neural networks with time-varying delays[J]. Mathematical Biosciences and Engineering, 2024, 21(2): 3319-3334. doi: 10.3934/mbe.2024147

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Abstract

The synchronization of inertial complex-valued memristor-based neural networks (ICVMNNs) with time-varying delays was explored in the paper with the non-separation and non-reduced approach. Sufficient conditions required for the exponential synchronization of the ICVMNNs were identified with the construction of comprehensive Lyapunov functions and the design of a novel control scheme. The adaptive synchronization was also investigated based on the derived results, which is easier to implement in practice. What's more, a numerical example that verifies the obtained results was presented.

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