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Undersampling for the training of feedback neural networks on large sequences; application to the modeling of an induction machine

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4 Author(s)
Constant, L. ; Lab. d''Electrotech. et d''Electron. Ind., CNRS, Toulouse, France ; Dagues, B. ; Rivals, I. ; Personnaz, L.

This paper proposes an economic method for the nonlinear modeling of dynamic processes using feedback neural networks, by undersampling the training sequences. The undersampling (i) allows a better exploration of the operating range of the process for a given size of the training sequences, and (ii) it speeds up the training of the feedback networks. This method is successfully applied to the training of a neural model of the electromagnetic part of an induction machine, whose sampling period must be small enough to take fast variations of the input voltage into account, i.e, smaller than 1 μs

Published in:

Electronics, Circuits and Systems, 1999. Proceedings of ICECS '99. The 6th IEEE International Conference on  (Volume:2 )

Date of Conference:

5-8 Sep 1999