Enhancement of Reservoir Computing Performance via Ultrashort Optical Feedback

Abstract An enhanced delay-based photonic reservoir computing system assisted by ultrashort optical feedback is proposed and numerically demonstrated. Specifically, a picosecond-scale ultrashort optical feedback loop is integrated into a conventional semiconductor laser-based single-feedback reservoir computing (RC) architecture. The ultrashort feedback loop acts as a fast dynamical regulator, enabling multiscale temporal dynamics while largely preserving the structural simplicity of the single-feedback configuration. The RC performance is evaluated in terms of separability and consistency. The results show that appropriately selected ultrashort feedback parameters can increase between-class separation and reduce within-class dispersion while maintaining reproducible reservoir responses. Moreover, the memory capacity performance and benchmark evaluations using Santa Fe chaotic time-series prediction and nonlinear channel equalization demonstrate that the proposed system outperforms the conventional single-feedback reservoir.

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Publication Details

Journal
Chinese Physics B
Published
2026-10-28
DOI
https://doi.org/10.1088/1674-1056/aeba67
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
0.00

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Enhancement of Reservoir Computing Performance via Ultrashort Optical Feedback

Zebin Li, Chenpeng Xue, Ting Li, Zuxing 祖兴 Zhang 张 et al.
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Chinese Physics B
Neural Networks and Reservoir Computing
article

Enhancement of Reservoir Computing Performance via Ultrashort Optical Feedback

Zebin Li, Chenpeng Xue, Ting Li, Zuxing 祖兴 Zhang 张, Yanhua Hong
article en

Abstract

Abstract An enhanced delay-based photonic reservoir computing system assisted by ultrashort optical feedback is proposed and numerically demonstrated. Specifically, a picosecond-scale ultrashort optical feedback loop is integrated into a conventional semiconductor laser-based single-feedback reservoir computing (RC) architecture. The ultrashort feedback loop acts as a fast dynamical regulator, enabling multiscale temporal dynamics while largely preserving the structural simplicity of the single-feedback configuration. The RC performance is evaluated in terms of separability and consistency. The results show that appropriately selected ultrashort feedback parameters can increase between-class separation and reduce within-class dispersion while maintaining reproducible reservoir responses. Moreover, the memory capacity performance and benchmark evaluations using Santa Fe chaotic time-series prediction and nonlinear channel equalization demonstrate that the proposed system outperforms the conventional single-feedback reservoir.

Chinese Physics B
Bangor University (GB), Nanjing University of Posts and Telecommunications (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 14%
Neural Networks and Reservoir Computing
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