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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/22492

Title: Comparative study on prediction of fuel cell performance using machine learning approaches
Authors: Mao, Lei
Jackson, Lisa M.
Keywords: Fuel cell
Machine learning technique
Neural network
Adaptive neuro-fuzzy inference system
Particle filtering approach
Issue Date: 2016
Publisher: Published by Newswood Limited for IAENG
Citation: 2016. Comparative study on prediction of fuel cell performance using machine learning approaches. IN: Ao, S.I. ...et al. (eds.) Lecture Notes in Engineering and Computer Science: Proceedings of The International MultiConference of Engineers and Computer Scientists 2016, 16-18 March, 2016, Hong Kong, pp. 52-57.
Abstract: This paper provides a comparative study to evaluate the effectiveness of machine learning techniques in predicting fuel cell performance. Several methods applied in fuel cell prognostics are selected, including a neural network, an adaptive neuro-fuzzy inference system, and a particle filtering approach. Test data from a fuel cell system is used for the evaluation. From the results, the advantages and disadvantages of these approaches are compared, which can provide a general framework for the selection of the necessary algorithms for fuel cell prognostics under different conditions.
Description: This is a conference paper.
Sponsor: This work is supported by grant EP/K02101X/1 for Loughborough University, Department of Aeronautical and Automotive Engineering from the UK Engineering and Physical Sciences Research Council (EPSRC).
Version: Accepted for publication
URI: https://dspace.lboro.ac.uk/2134/22492
Publisher Link: http://www.iaeng.org/publication/IMECS2016/IMECS2016_pp52-57.pdf
ISBN: 9789881925381
ISSN: 2078-0966
Appears in Collections:Conference Papers and Contributions (Aeronautical and Automotive Engineering)

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