期刊
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
卷 352, 期 2, 页码 669-685出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jfranklin.2014.04.021
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资金
- Canadian Natural Resources and Applied Sciences Endowment Fund
- Electric Mobility PHEV Program of Natural Resource of Canada
- China Scholarship Council
In this work, a more efficient and robust driving pattern recognition technique, extended Support Vector Machine (SVM) with embedded feature selection ability, has been introduced. Besides statistical significance, this proposed SVM also takes into account the accessibility and reliability of features during feature selection, so as to enable the driving condition discrimination system to achieve higher recognition efficiency and robustness. The recognition results of this extended SVM are compared with results from standard 2-norm SVM and linear 1-norm SVM, using representative driving cycle data to demonstrate the function and superiority of the new technique. (C) 2014 Published by Elsevier Ltd. on behalf of The Franklin Institute
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