A novel feature susceptibility approach for a PEMFC control system based on an improved XGBoost-Boruta algorithm

A novel feature susceptibility approach for a PEMFC control system based on an improved XGBoost-Boruta algorithm

Data-driven modelling methods are being developed in the quest to achieve more accurate performance prediction of protons exchange membrane fuel cell (PEMFC) systems in response to their complicated physicochemical phenomena. However, there is little research in this field detailing the pre-processi...

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Bibliographic Details
Main Authors: Xinjie Yuan, Fujun Chen, Zenggang Xia, Linlin Zhuang, Kui Jiao, Zhijun Peng, Bowen Wang, Richard Bucknall, Konrad Yearwood, Zhongjun Hou
Format: Article
Language:English
Published: Elsevier 2023-04-01
Series:Energy and AI
Subjects:
Boruta
Extreme gradient boosting (XGBoost)
Feature selection
Proton exchange membrane fuel cell (PEMFC)
Online Access:http://www.sciencedirect.com/science/article/pii/S2666546823000010

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http://www.sciencedirect.com/science/article/pii/S2666546823000010

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