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

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 /spl mu/s.