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Reservoir Computing and Task Performing through Using High-β Lasers with Delayed Optical Feedback
Progress In Electromagnetics Research, Vol. 178, 1-12, 2023
Nonlinear photonic sources including semiconductor lasers have been recently utilized as ideal computation elements for information processing. They supply energy-efficient way and rich dynamics for classification and recognition tasks. In this work, we propose and numerically study the dynamics of complex photonic systems including high-β laser element with delayed feedback and functional current modulation, and employ nonlinear laser dynamics of near-threshold region for the application in reservoir computing. The results indicate a perfect (100%) recognition accuracy for the pattern recognition task and an accuracy about 98% for the Mackey-Glass chaotic sequences prediction. Therefore, the system shows an improvement of performance with low-power consumption. In particular, the error rate is an order of magnitude smaller than previous works. Furthermore, by changing the DC pump, we are able to modify the number of spontaneous emission photons of the system, which then allows us to explore how the laser noise impacts the performance of the reservoir computing system. Through manipulating these variables, we show a deeper understanding on the proposed system, which is helpful for the practical applications of reservoir computing.
Tao Wang, Can Jiang, Qing Fang, Xingxing Guo, Yahui Zhang, Chaoyuan Jin, and Shuiying Xiang, "Reservoir Computing and Task Performing through Using High-β Lasers with Delayed Optical Feedback," Progress In Electromagnetics Research, Vol. 178, 1-12, 2023.

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