Automatic Generation of Initial Weights and Estimation of Hidden Units for Pattern Classifcation Using Neural Networks

Abstract

This study high lights on the subject of weight initialization in back-propagation feed-forward networks. Training data is analyzed and the notion of critical points is introduced for determining the initial weights and the number of hidden units. The proposed method has been applied to artificial data and the publicly available cancer database. The experimental outcomes indicate that the proposed method reduces training time and results in better solution.