Fuzzy clustering preprocessor in neural classifiers

Abstract

Radial basis function (RBF) neural networks form an essential category of architectures of neurocomputing. They exhibit interesting and useful properties of stable and fast learning associated with significant generalization capabilities. This successful performance of RBF neural networks can be attributed to the use of a collection of properly selected RBFs. In this way this category of the networks strongly relies on some domain knowledge about a classification problem at hand. Following this vein, this study introduces fuzzy clustering, and fussy isodata, in particular, as an efficient tool aimed at constructing receptive fields of RBF neural networks. It is shown that the functions describing these fields are completely derived as a by‐product of fuzzy clustering and do not require any further tedious refinements. The efficiency of the design is illustrated with the use of synthetic two‐dimensional data as well as real‐world highly dimensional ECG patterns. The classification of the latter data set clearly points out advantages of RBF neural networks in pattern recognition problems.