Distributed normalisation input coding to speedup training processof BP-neural network classifier

20 July 1995

journal article
Published by Institution of Engineering and Technology (IET) in Electronics Letters

Vol. 31 (15) , 1267-1269
https://doi.org/10.1049/el:19950854

Abstract

A coding method, distributed normalisation, is presented to speed up the training process of a back-propagation neural network classifier. In contrast to one-node normalisation coding, the values of the feature variables are distributed over a number of input nodes to increase the representation range of certain parts of each feature variable. A distinct advantage of this coding method is its ability to maintain the generalisation capability of one-node normalisation coding.

Keywords

BP-NEURAL NETWORK CLASSIFIER
INPUT NODES
REPRESENTATION RANGE
DISTRIBUTED NORMALISATION INPUT CODING
FEATURE VARIABLES
GENERALISATION CAPABILITY
TRAINING PROCESS

This publication has 1 reference indexed in Scilit:

Theory of the backpropagation neural network
Published by Institute of Electrical and Electronics Engineers (IEEE) ,1989