Short-term prediction for chaotic time series based on photonic reservoir computing using VCSEL with feedback loop

• Xingxing Guo, Hanxu Zhou, shui xiang, Qian Yu, YAHUI ZHANG, Yanan Han, Tao Wang, and Yue Hao
• received 01/03/2024; accepted 03/18/2024; posted 03/19/2024; Doc. ID 517275
• Abstract: Chaos, occurring in a deterministic system, has permeated various fields such as mathematics, physics, and life science. Consequently, the prediction of chaotic time series has received widespread attention and made significant progress. However, many problems, such as high computational complexity and difficulty in hardware implementation, could not be solved by existing scheme. To overcome the problems, we employ the chaotic system of VCSEL mutual coupling network to generate chaotic time series through optical system simulation and experimentation in this paper. Furthermore, a photonic reservoir computing based on VCSEL, along with feedback loop, is proposed for the short-term prediction of the chaotic time series. The relationship between the prediction difficulty of the RC computing system and the difference in complexity of the chaotic time series has been studied with emphasis. Additionally, the attention coefficient of injection strength and feedback strength, prediction duration and other factors on system performance are considered in both simulation and experiment. The use of RC system to predict the chaotic time series generated by actual chaotic systems is significant for expanding the practical application scenarios of the RC.