Knowledge extraction and insertion from radial basis function networks

Neural networks provide excellent solutions for pattern recognition and classification problems. Unfortunately, in the case of distributed neural networks such as the multilayer perceptron it is difficult to comprehend the learned internal mappings. This makes any form of explanation facility such as that possessed by expert systems impractical. However, in the case of localist neural representations the situation is more transparent to examination. This paper examines the quality and comprehensibility of rules extracted from localist neural networks, specifically the radial basis function network. The rules are analysed in order to gain knowledge and insight into the data. We also investigate the benefits of inserting prior domain knowledge into a radial basis function network.