Photonics Research, Volume. 13, Issue 3, 728(2025)
Heterogeneous forecasting of chaotic dynamics in vertical-cavity surface-emitting lasers with knowledge-based photonic reservoir computing
Fig. 1. Heterogeneous forecasting scheme using photonic time-delayed reservoir computing combined with an imperfect dynamical model.
Fig. 2. Predicted performance NMSE of isolated TDRC: (a) NMSE as a function of the input gain coefficient
Fig. 3. Predictive performance of isolated TDRC (a), (b) compared to knowledge-based photonic reservoir computing with respect to error coefficients
Fig. 4. Predictive performance for chaotic dynamics generated by a VCSEL with (a) isolated TDRC and (b) heterogeneous TDRC; the prediction error between the original data and isolated TDRC (c), heterogeneous predicted signal (d).
Fig. 5. Robustness of the prediction performance of heterogeneous TDRC and isolated TDRC for different original data. NMSE as a function of self-feedback strength
Fig. 6. Effects of the complexity of the original signal on predictive performance. Evolutions of the polarization state plotted on the normalized Poincaré sphere for polarizer angles (a)
Fig. 7. Two-dimensional evolution of NMSE in parameter spaces of
Fig. 8. Prediction accuracy NMSE of heterogeneous TDRC and isolated TDRC with different number of virtual nodes
Fig. 9. Diagram of the heterogeneous TDRC experimental setup (includes the input and reservoir layers shown in Fig.
Fig. 10. Predictive performance NMSE of isolated TDRC and heterogeneous TDRC under different (a) self-feedback strengths with
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Liyue Zhang, Chenkun Huang, Songsui Li, Wei Pan, Lianshan Yan, Xihua Zou, "Heterogeneous forecasting of chaotic dynamics in vertical-cavity surface-emitting lasers with knowledge-based photonic reservoir computing," Photonics Res. 13, 728 (2025)
Category: Optoelectronics
Received: Jul. 31, 2024
Accepted: Jan. 12, 2025
Published Online: Mar. 3, 2025
The Author Email: Liyue Zhang (lyzhang@swjtu.edu.cn)