On optimal usage of centroid units in CMLP network

In our recent studies we have investigated a centroid based multilayer perceptron (CMLP) network architecture for nonlinear modelling purposes. In CMLP network the first hidden layer is a centroid layer which performs a circular transformation for the input data. The output of this centroid layer is then fed to a usual multilayer perceptron network. We have found that this 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 has been determined empirically. Here we extend our work by introducing a method for determining the minimum or near minimum number of units for the centroid layer in a given problem. The proposed scheme is based on the usage of principal component analysis. The presented benchmark simulations demonstrate that the proposed method can effectively discover the minimum number of centroid units. However such a minimum structure is shown to be relatively sensitive for the weight initialization