Stability analysis of Clifford-valued memristor-based neural networks with impulsive disturbances and its application to image encryption

Ningning Zhao and Yuanhua Qiao

Applied Mathematics and Computation, 2024, vol. 475, issue C

Abstract: In this paper, a type of delayed Clifford-valued memristor-based neural networks (CLVMNNs) with impulsive disturbances is established, and the global exponential stability is investigated by using generalized norm. Firstly, the n-dimensional Clifford-valued systems are decomposed into 2mn-dimensional real-valued systems to address the non-commutativity problem of the multiplication of Clifford numbers. Secondly, the generalized ∞-norm and 1-norm are introduced to induce the global exponential stability for CLVMNNs, and two special Lyapunov functionals are established to prove the stability. Thirdly, the strict assumption of the boundedness of activation function in previous research is loosened, and some less conservative conditions of stability are obtained based on the constructed Lyapunov functionals. Finally, the theoretical results are verified by two numerical simulations, and an image encryption scheme is proposed to show the application in real world situation based on the delayed CLVMNNs.

Keywords: Clifford-valued memristor-based neural networks; Generalized norm; Exponential stability; Impulsive disturbances; Image encryption (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:475:y:2024:i:c:s0096300324001826

DOI: 10.1016/j.amc.2024.128710

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