By Topic

On the initialization and optimization of multilayer perceptrons

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

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

Multilayer perceptrons are now widely used for pattern recognition, although the training remains a time consuming procedure often converging toward a local optimum. Moreover, as the optimum network size and topology are usually unknown, the search of this optimum requires a lot of networks to be trained. In this paper the authors propose a method for properly initializing the parameters (weights) of a two-layer perceptron, and for identifying (without the need for any error-backpropagation training) the most suitable network size and topology for solving the problem under investigation. The initialized network can then be optimized by means of the standard error-backpropagation (EBP) algorithm. The authors' method is applicable to any two-layer perceptron comprising concentric as well as squashing units on its hidden layer. The output units are restricted to squashing units, but direct connections from the input to the output layer are also accommodated. To illustrate the power of the method, results obtained for different classification tasks are compared to similar results obtained using a traditional error-backpropagation training starting from a random initial state

Published in:

IEEE Transactions on Neural Networks  (Volume:5 ,  Issue: 5 )