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Transactions ID Number: 19-289
Full Name: Ryad Zemouri
Position: Associate Professor
Age: ON
Sex: Male
Address: Cnam 2 rue Conté Accès 35 - 3e étage
Country: FRANCE
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E-mail address: ryad.zemouri@cnam.fr
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Title of the Paper: mproving the prediction accuracy of Recurrent neural network by a PID controller
Authors as they appear in the Paper: Ryad Zemouri, Rafael Gouriveau, Paul Ciprian PATIC
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Number of paper pages: 16
Abstract: In maintenance field, prognostic is recognized as a key feature as the prediction of the remaining useful life of a system which allows avoiding inopportune maintenance spending. Assuming that it can be difficult to provide models for that purpose, artificial neural networks appear to be well suited. In this paper, an approach combining a Recurrent Radial Basis Function network (RRBF) and a proportional integral derivative controller (PID) is proposed in order to improve the accuracy of predictions. The PID controller attempts to correct the error between the real process variable and the neural network predictions.
Keywords: Maintenance, prognostic, error of prediction, neural network, RRBF, PID
EXTENSION of the file: .pdf
Special (Invited) Session: Prediction Error Feedback for Time Series Prediction: a way to improve the accuracy of predictions,
Organizer of the Session: 634-178
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