Knowledge extraction from deep convolutional neural networks applied to cyclo-stationary time-series classification

Research output: Contribution to journalArticlepeer-review

3 Scopus citations
Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalInformation Sciences
Volume524
DOIs
StatePublished - 1 Jul 2020

Bibliographical note

Funding Information:
The work was sponsored in part by GIDTEC Project No. 003-002-2016-03-03 and the National Natural Science Foundation of China (Grant No. 51775112). The experimental work was developed at the GIDTEC Research Group Lab of the Universidad Politcnica Salesiana, Cuenca, Ecuador. This research was also partially supported by the projects TIN2017-82113-C2-1-R (VICTORY Project) and TIN2013-41086-P (LOCOCIDA Project), both from Ministerio de Econom?a, Industria y Competitividad of Spain, with FEDER funds from the European Union.

Funding Information:
The work was sponsored in part by GIDTEC Project No. 003-002-2016-03-03 and the National Natural Science Foundation of China (Grant No. 51775112 ). The experimental work was developed at the GIDTEC Research Group Lab of the Universidad Politcnica Salesiana, Cuenca, Ecuador. This research was also partially supported by the projects TIN2017-82113-C2-1-R (VICTORY Project) and TIN2013-41086-P (LOCOCIDA Project), both from Ministerio de Economía, Industria y Competitividad of Spain, with FEDER funds from the European Union.

Publisher Copyright:
© 2020 Elsevier Inc.

Keywords

  • Convolutional neural network
  • Cyclo-stationary time-series analysis
  • Deep learning
  • Fault diagnosis
  • Knowledge extraction

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