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Reservoir computing system using discrete memristor for chaotic temporal signal processing

Author

Listed:
  • Deng, Yue
  • Zhang, Shuting
  • Yuan, Fang
  • Li, Yuxia
  • Wang, Guangyi

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.

Suggested Citation

  • Deng, Yue & Zhang, Shuting & Yuan, Fang & Li, Yuxia & Wang, Guangyi, 2025. "Reservoir computing system using discrete memristor for chaotic temporal signal processing," Chaos, Solitons & Fractals, Elsevier, vol. 194(C).
    • Handle: RePEc:eee:chsofr:v:194:y:2025:i:c:s0960077925002437
    DOI: 10.1016/j.chaos.2025.116230
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    References listed on IDEAS

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