New Radial Basis Function Neural Network Architecture for Pattern Classification: First Results

摘要

This paper presents the initial results concerning a new Radial Basis Function Artificial Neural Network (RBFNN) architecture for pattern classification. Performance of the new architecture is demonstrated with different data sets. Its efficiency is also compared with different classification methods reported in literature: Multilayer Perceptron, Standard Radial Basis Neural Networks, KNN and Minimum Distance classifiers, showing a much better performance. Results are only given for problems using two features.