Optical reservoir computing based on the bagging trees

Mujun Xiao; Ailing Zhang; Chao Wang

doi:10.1117/12.3054316

20 January 2025 Optical reservoir computing based on the bagging trees

Mujun Xiao, Ailing Zhang, Chao Wang

Author Affiliations +

Proceedings Volume 13515, Fourth International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2024); 1351521 (2025) https://doi.org/10.1117/12.3054316
Event: 4th International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2024), 2024, Chongqing, China

Abstract

In traditional optical reservoir computing, the least squares method are commonly used to train the output weights for regression tasks. Although these algorithms are highly versatile, their training efficiency and accuracy are somewhat inferior compared to current algorithms. Bagging trees, an ensemble learning method, works by resampling the dataset to train multiple models and then combining the predictions of these models, thus improving the stability and accuracy of the final prediction. By combining optical reservoir computing with the bagging trees, both the prediction accuracy and training efficiency are greatly improved, with the highest R-squared prediction reaching 99.56%.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mujun Xiao, Ailing Zhang, and Chao Wang "Optical reservoir computing based on the bagging trees", Proc. SPIE 13515, Fourth International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2024), 1351521 (20 January 2025); https://doi.org/10.1117/12.3054316

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