In this paper we present the experimental results of the neural network
control of a servo-system in order to control its speed. The control strategy
is implemented by using an inverse-model control based on Artificial Neural
Networks (ANNs). The network training was performed using two learning
algorithms: Levenberg-Marquardt and Bayesian regularization. We evaluate the
generalization capability for each method according to both the correct
operation of the controller to follow the reference signal, and the control
efforts developed by the ANN-based controller.