Synchronization analysis of delayed quaternion-valued memristor-based neural networks by a direct analytical approach

Jun Guo; Yanchao Shi; Shengye Wang; Jun Guo; Yanchao Shi; Shengye Wang

doi:10.3934/era.2024156

Electronic Research Archive

2024, Volume 32, Issue 5: 3377-3395. doi: 10.3934/era.2024156

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Research article

Synchronization analysis of delayed quaternion-valued memristor-based neural networks by a direct analytical approach

1.
College of Applied Mathematics, Chengdu University of Information Technology, Chengdu 610225, China
2.
Key Laboratory of Numerical Simulation of Sichuan Provincial Universities, School of Mathematics and Information Sciences, Neijiang Normal Univeristy, Neijiang 641000, China
3.
School of Sciences, Southwest Petroleum University, Chengdu 610500, China

Received: 11 January 2024 Revised: 12 March 2024 Accepted: 22 April 2024 Published: 24 May 2024

This issue discusses the asymptotic synchronization and the exponential synchronization for memristor-based quaternion-valued neural networks under the time-varying delays. Some criteria for synchronization of the memristor-based quaternion-valued neural networks are given by exploiting the set-valued theory, the differential inclusion theory, some analytic techniques, as well as constructing novel controllers, It is worth noting that the synchronization problem about the memristor-based quaternion-valued neural networks were studied by the direct analysis method in this paper. Finally, the main theoretical results were verified by numerical simulations.
- exponential synchronization,
- memristor-based neural networks,
- memristor,
- quaternion-valued
Citation: Jun Guo, Yanchao Shi, Shengye Wang. Synchronization analysis of delayed quaternion-valued memristor-based neural networks by a direct analytical approach[J]. Electronic Research Archive, 2024, 32(5): 3377-3395. doi: 10.3934/era.2024156

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Abstract

This issue discusses the asymptotic synchronization and the exponential synchronization for memristor-based quaternion-valued neural networks under the time-varying delays. Some criteria for synchronization of the memristor-based quaternion-valued neural networks are given by exploiting the set-valued theory, the differential inclusion theory, some analytic techniques, as well as constructing novel controllers, It is worth noting that the synchronization problem about the memristor-based quaternion-valued neural networks were studied by the direct analysis method in this paper. Finally, the main theoretical results were verified by numerical simulations.

References

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