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An equalized error backpropagation algorithm for the on-line training of multilayer perceptrons

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2 Author(s)
Martens, J.-P. ; Electron. & Inf. Syst., Ghent Univ., Gent, Belgium ; Weymaere, N.

The error backpropagation (EBP) training of a multilayer perceptron (MLP) may require a very large number of training epochs. Although the training time can usually be reduced considerably by adopting an on-line training paradigm, it can still be excessive when large networks have to be trained on lots of data. In this paper, a new on-line training algorithm is presented. It is called equalized EBP (EEBP), and it offers improved accuracy, speed, and robustness against badly scaled inputs. A major characteristic of EEBP is its utilization of weight specific learning rates whose relative magnitudes are derived from a priori computable properties of the network and the training data

Published in:

Neural Networks, IEEE Transactions on  (Volume:13 ,  Issue: 3 )

Date of Publication:

May 2002

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