Extension Of Generalized Delta Rule To Adapt Sigmoid Functions

Abstract

In this paper an extension of the generalized delta rule to adapt sigmoid functions is presented. The proposed learning procedure retains the standard generalized delta rule for weights changes and introduces changes of the sigmoid functions parameters in the same fashion. The adaptation of the nonlinearities so introduced allows to expect an acceleration of the learning and a better adaptation to the input pattern distribution.