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Towards accurate and reproducible predictions for prognostic : an approach combining a RRBF Network and an AutoRegressive Model.

Abstract : In prognostic's field, the lack of knowledge on the behavior of equipments can impede the development of classical dependability analysis, or the building of effective physic-based models. Following that, artificial neural networks (ANNs) appear to be well suited since they can learn from data gathered from equipments. In this paper, an approach combining a Recurrent Radial Basis Function network (RRBF) and an AutoRegressive with eXogenous inputs model (ARX) is proposed in order to perform the prediction step of prognostics: the ARX attempts to correct the error of predictions of the RRBF. Moreover, since performances of an ANN can be closely related to initial parameterization of the network, a criterion is defined to quantify the reproducibility of predictions and thereby a priori estimate the usefulness of neural network structure. The whole aims at improving the prediction step of prognostics, which is critical with respects to real applicative conditions.
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https://hal.archives-ouvertes.fr/hal-00503906
Contributor : Martine Azema <>
Submitted on : Monday, July 19, 2010 - 11:55:09 AM
Last modification on : Thursday, November 12, 2020 - 9:42:04 AM
Long-term archiving on: : Friday, October 22, 2010 - 3:43:27 PM

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  • HAL Id : hal-00503906, version 1

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Ryad Zemouri, Rafael Gouriveau. Towards accurate and reproducible predictions for prognostic : an approach combining a RRBF Network and an AutoRegressive Model.. 1st IFAC Workshop on Advanced Maintenance Engineering, Services and Technology, IFAC A-MEST'10., Jul 2010, Lisbonne, Portugal. pp.163-168. ⟨hal-00503906⟩

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