Improving the prediction accuracy of recurrent neural network by a PID controller.

Ryad Zemouri (1) , Rafael Gouriveau (2) , Paul Ciprian Patic (3)

1 CEDRIC - LAETITIA - CEDRIC. Traitement du signal et architectures électroniques
2 FEMTO-ST - Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174)
3 UVT - Valahia University of Targoviste [Roumanie]

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.

Mots clés: PID; Maintenance; prognostic; error of prediction; neural network; RRBF; PID.

Domaines: Automatique / Robotique

Ryad Zemouri, Rafael Gouriveau, Paul Ciprian Patic. Improving the prediction accuracy of recurrent neural network by a PID controller.. International Journal of Systems Applications, Engineering & Development., 2010, 4 (2), pp.19-34. ⟨hal-00544722⟩