Optimization of a learning algorithm for tactile pattern generation

In this paper we present an optimization method for a learning algorithm for tactile stimuli generation which are adapted by means of tactile perception of a human. Because of special requirements for a learning algorithm for tactile perception tuning the optimization cannot be performed based on gradient-descent or likelihood estimation methods. Therefore an automatic tactile classification (ATC) is introduced for the optimization process. The results show that the ATC equals the tactile comparison of humans and that the learning algorithm is successfully optimized by means of the ATC.