An inverse modeling using a five-layer perceptron
S. Yamaguchi, Masahiro Tanaka, Hidekiyo Itakura · 2005
This paper shows a learning algorithm for an inverse model of a system using a five-layer perceptron. In the learning algorithm, two performance indexes are used: one is an index for the forward model of the system and the other is for the inverse model. The algorithm reduces these two performance indexes at the same time. As a result, the forward model and the inverse model are formed in the perceptron. The algorithm is applied to the learning of inverse kinematics and dynamics models of manipulators by computer simulations. By the simulation experiments, it is confirmed that the algorithm can learn the inverse models effectively.