Learning rules of neural networks for inverse systems
Yutaka Maeda · Electronics and Communications in Japan (Part III Fundamental Electronic Science) · 1993
Abstract This paper considers on‐line learning of neural networks for an inverse problem. A neural network must learn an inverse system of a static unknown system on the basis of an evaluation function that is a squared error between an output of the unknown system and its desired value. Usual learning methods need information about the sign of the sensitivity of the unknown system. This paper proposes learning rules that do not require the information about that sign. These rules utilize a perturbation signal. Rules with the perturbation on an input of the unknown system and a rule with it on each weight of the neural network are presented. The usefulness of these learning rules is confirmed via some numerical simulations.