Determining the number of centroids for CMLP network.

Abstract

In our recent studies we have proposed and investigated a centroid-based multilayer perceptron (CMLP) network architecture for modelling purposes. In the CMLP network the first hidden layer is a centroid layer. We have found that the proposed hybrid can provide significant advantages over standard multilayer perceptron networks in terms of fast and efficient learning, and compact network structure in complex classification problems. Previously the number of units for the centroid layer had been determined empiricially. Here we extend our work by introducing a method for determining the minimal number of centroid units for a given problem. The proposed scheme also enables efficient initialization of the centroids units. In addition, we also propose an initialization scheme for the MLP part of the CMLP network. Our benchmark simulations show that the proposed methods significantly improve the CMLP scheme.