Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/151454
Title: Machine learning and silicon photonic sensor for complex chemical components determination
Authors: Zhang, Hui
Karim, Muhammad Faeyz
Zheng, Shaonan
Cai, Hong
Gu, Yuandong
Chen, Shoushun
Yu, Hao
Liu, Ai Qun
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Zhang, H., Karim, M. F., Zheng, S., Cai, H., Gu, Y., Chen, S., Yu, H. & Liu, A. Q. (2018). Machine learning and silicon photonic sensor for complex chemical components determination. 2018 Conference on Lasers and Electro-Optics (CLEO), 1-2. https://dx.doi.org/10.1364/CLEO_AT.2018.JW2A.54
Project: NRF-CRP13-2014-01
Abstract: We propose an integrated microring resonator sensing system based on Backward-Propagation Neural Networks (BPNN)-Adaboost algorithm to predict component fraction in binary liquid mixtures. A minimum absolute error of 0.0023 and mean squared error of 0.000345 is achieved by this training model.
URI: https://hdl.handle.net/10356/151454
ISBN: 978-1-943580-42-2
DOI: 10.1364/CLEO_AT.2018.JW2A.54
Rights: © 2018 The Author(s). All rights reserved. This paper was published in Proceedings of 2018 Conference on Lasers and Electro-Optics (CLEO) and is made available with permission of The Author(s).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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