Reservoir computing system using discrete memristor for chaotic temporal signal processing

Yue Deng, Shuting Zhang, Fang Yuan, Yuxia Li and Guangyi Wang

Chaos, Solitons & Fractals, 2025, vol. 194, issue C

Abstract: Reservoir computing (RC) is a highly efficient neural network for processing temporal signals, primarily due to its significantly lower training cost compared to standard recurrent neural networks. In this work, a novel discrete memristor (DM) model is investigated and a simple two-dimensional chaotic map based on the DM model is presented, in which complex dynamics are simulated. By utilizing this DM-based map as a reservoir, a dynamic DM-based RC system is constructed, and the performance is verified through nonlinear regression and time-series prediction tasks. Our system achieves a high accuracy rate of 99.99 % in the nonlinear recognitions, as well as a low root mean square error of 0.0974 in the time-series prediction of the Logistic map. This work may pave the way for the future development of high-efficiency memristor-based RC systems to handle more complex temporal tasks.

Keywords: Memristor; Chaos; Chaotic map; Reservoir computing (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:194:y:2025:i:c:s0960077925002437

DOI: 10.1016/j.chaos.2025.116230

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