Approximate multi-state reliability expressions using a new machine learning technique (Articolo in rivista)

Type
Label
  • Approximate multi-state reliability expressions using a new machine learning technique (Articolo in rivista) (literal)
Anno
  • 2005-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1016/j.ress.2004.08.023 (literal)
Alternative label
  • C. M. Rocco, M. Muselli (2005)
    Approximate multi-state reliability expressions using a new machine learning technique
    in Reliability engineering & systems safety; Elsevier Science Ltd., Oxford (Regno Unito)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • C. M. Rocco, M. Muselli (literal)
Pagina inizio
  • 261 (literal)
Pagina fine
  • 270 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 89 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 3 (literal)
Note
  • ISI Web of Science (WOS) (literal)
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • C. M. Rocco: Facultad de Ingeniería, Universidad Central, Caracas, Venezuela, M. Muselli CNR IEIIT Genova (literal)
Titolo
  • Approximate multi-state reliability expressions using a new machine learning technique (literal)
Abstract
  • The machine-learning-based methodology, previously proposed by the authors for approximating binary reliability expressions, is now extended to develop a new algorithm, based on the procedure of Hamming Clustering, which is capable to deal with multi-state systems and any success criterion. The proposed technique is presented in details and verified on literature cases: experiment results show that the new algorithm yields excellent predictions. (literal)
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