Comparison of Learning Methods in the Learning Feed-forward Control Setting

This thesis details the comparison between several learning methods that are to be used in the Learning Feed-forward Control (LFFC) setting. These learning methods consist of the Multilayer Perceptron (MLP), the Radial-Basis Function Network (RBFN), the Support Vector Machine (SVM), the Least Square Support Vector Machine (LSSVM), and the Key Sample Machine (KSM). The comparison is performed by simulations with respect to reproducibility - the ability to train various training sets from the same function, high dimensional inputs - the ability to train samples from a high dimensional function, and real-world data of a linear motor - the ability to train samples from the motion of a linear motor. Simulation results show that there is no best method. The reproducibility comparison shows that the RBFN from visual design is the best as it gives the smallest variance. The mean square error of the high dimensional inputs comparison shows that the KSM is the best. From approximation of linear motor data, the KSM also gives the best result with respect to mean square error.