CBP networks as a generalized neural model

Sandro Ridella, Stefano Rovetta, and Rodolfo Zunino
Department of Biophysical and Electronics Engineering -
University of Genova, Italy



Proceedings of the 1997 International Conference on
Neural Networks, Houston, USA, June 1997, pp. 210-214. 



Abstract

This paper analyzes the Circular backpropagation network, 
a simple modification of the multilayer perceptron with
interesting practical properties, especially well-suited
to cope with pattern classification tasks. The proposed
model unifies the two main representation paradigms found
in the class of mapping networks for classification, namely,
the surface-based and the prototype-based schemes, while
retaining the advantage of being trainable by
back-propagation. Multi-layer perceptrons,
Radial-Basis-Function networks and Vector-Quantization
networks are shown to be implementable with small
modifications to the model under study