Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/138831
Title: Machine learning and silicon photonic sensor for complex chemical components determination
Authors: Zhang, Haochi
Muhammad Faeyz Karim
Zheng, Shaonan
Cai, H.
Gu, Y. D.
Chen, Shou Shun
Yu, H.
Liu, Ai Qun
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Zhang, H., Muhammad Faeyz Karim, Zheng, S. N., Cai, H., Gu, Y. D., Chen, S. S., . . . Liu, A. Q. (2018). Machine learning and silicon photonic sensor for complex chemical components determination. 2018 Conference on Lasers and Electro-Optics (CLEO): Applications and Technology, JW2A-54-. doi:10.1364/CLEO_AT.2018.JW2A.54
metadata.dc.contributor.conference: 2018 Conference on Lasers and Electro-Optics (CLEO): Applications and Technology
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/10356/138831
ISBN: 9781943580422
DOI: 10.1364/CLEO_AT.2018.JW2A.54
Schools: School of Electrical and Electronic Engineering 
Rights: © 2018 The Author(s). All rights reserved. This paper was published by Optical Society of America (OSA) in 2018 Conference on Lasers and Electro-Optics (CLEO): Applications and Technology 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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