@inproceedings{260ab398eaa147e39eda0ae313e435d6,
title = "Component Based and Machine Learning Aided Optimal Filter Design for Full-Bridge Current Doubler Rectifier",
abstract = "Full-bridge current doubler rectifier topology is used to restrict the ripple of output current and quicken the dynamic response. However, mass and power loss of filter composed of passive components are large. To optimize the output filter parameters, this paper adopts machine learning (ML) methods to train a support vector machine (SVM) model and an artificial neural network (ANN) model using data samples collected from simulation. SVM is used to judge the feasibility of filter design parameters, and the trained ANN serves as a dedicated surrogate model mapping from the design variables to the two optimization objectives (mass and power loss). After the ML aided filter optimization, the filter prototype based on the optimal design point is manufactured and tested on an experiment platform for the method validation.",
keywords = "Full-bridge current doubler rectifier, artificial neural network, filter optimization, machine learning, support vector machine",
author = "Liu, \{Gui Hua\} and Chen, \{Yan Bo\} and Yuan Gao and Zhu, \{Jia Ning\} and Wang, \{Bo Xin\} and Tao Yang",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 47th Annual Conference of the IEEE Industrial Electronics Society, IECON 2021 ; Conference date: 13-10-2021 Through 16-10-2021",
year = "2021",
month = oct,
day = "13",
doi = "10.1109/IECON48115.2021.9589272",
language = "英语",
series = "IECON Proceedings (Industrial Electronics Conference)",
publisher = "IEEE Computer Society",
booktitle = "IECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society",
address = "美国",
}