Training the Recurrent neural network by the Fuzzy Min-Max algorithm for fault prediction

Ryad Zemouri 1 Daniel Racoceanu 2 Noureddine Zerhouni 2 Eugénia Minca 3 Florin Filip 4
1 CEDRIC - LAETITIA - CEDRIC. Traitement du signal et architectures électroniques
CEDRIC - Centre d'études et de recherche en informatique et communications
2 LAB - Laboratoire d'automatique de Besançon
3 Dep. of Automation, Computer Science and Elec. Eng
4 Romanian Academy-INCE
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Conference papers
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https://hal-cnam.archives-ouvertes.fr/hal-02479235
Contributor : Ryad Zemouri <>
Submitted on : Friday, February 14, 2020 - 1:35:02 PM
Last modification on : Saturday, February 15, 2020 - 1:36:47 AM

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CNAM | UNIV-FCOMTE | FEMTO-ST | CEDRIC-CNAM | CNRS

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Ryad Zemouri, Daniel Racoceanu, Noureddine Zerhouni, Eugénia Minca, Florin Filip. Training the Recurrent neural network by the Fuzzy Min-Max algorithm for fault prediction. INTELLIGENT SYSTEMS AND AUTOMATION: 2nd Mediterranean Conference on Intelligent Systems and Automation (CISA’09), Mar 2009, Zarzis (Tunisia), France. pp.85-90, ⟨10.1063/1.3106518⟩. ⟨hal-02479235⟩

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