Improvement of Cluster Detection and Labeling Neural Network by Introducing Elliptical Basis Function

This paper proposes an improvement of the Cluster Detection and Labeling Neural Network. The original classifier criterion has been modified by introducing Elliptical Basis Functions (EBF) as transfer function of the hidden neurons. In the original CDL network, a similarity criterion is used to determine the membership to prototypes and then to classes. By introducing EBF, we have introduced degrees of membership leading to elliptic shape of classes. In this paper, the functioning of the original CDL network is summarized. Then, the improvements of the architecture in terms of network architecture, neuron activation function and learning stages are described. We present the improvement with EBF and the modification of the auto-adaptation neural network abilities. As validations of our architecture, we illustrate its benefits in comparison with the original CDL network.