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Intelligent fault diagnosis of rotating machinery using support vector machine with ant colony algorithm for synchronous feature selection and parameter optimization

Crossref DOI link: https://doi.org/10.1016/j.neucom.2015.04.069

Published Print: 2015-11

Update policy: https://doi.org/10.1016/elsevier_cm_policy

Authors

Zhang, XiaoLi

Chen, Wei

Wang, BaoJian

Chen, XueFeng
Funding

Funding for this research was provided by:

National Natural Science Foundation of China (51405028)

National Natural Science Foundation of China (51175509)

National Key Basic Research Program of China (2015CB057400)

Chang׳ an University (2013G1502046)
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Text and Data Mining valid from 2015-11-01
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This article is maintained by: Elsevier

Article Title: Intelligent fault diagnosis of rotating machinery using support vector machine with ant colony algorithm for synchronous feature selection and parameter optimization

Journal Title: Neurocomputing

CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.neucom.2015.04.069

Content Type: article

Copyright: Copyright © 2015 Elsevier B.V. All rights reserved.

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