A simplified forward-propagation learning rule applied to adaptive closed-loop control

Abstract

In terms of computational neuroscience, several theoretical learning schemes have been proposed to acquire suitable motor controllers in the human brain. The controllers have been classified into a feedforward manner and a feedback manner as inverse models of controlled objects. For learning a feedforward controller, we have proposed a forward-propagation learning (FPL) rule which propagates error "forward" in a multi-layered neural network to solve a credit assignment problem. In the current work, FPL is simplified to realize accurate learning, and to be extended to adaptive feedback control. The suitability of a proposed scheme is confirmed by computer simulation. © Springer-Verlag Berlin Heidelberg 2005.