A new method for generating fuzzy classification systems using RBF neurons with extended RCE learning

A new method is presented combining the advantages of fuzzy inference and neural network learning. A three-layer radial basis function (RBF) network is used to extract rules and to identify the necessary membership functions of the inputs for a fuzzy classification system. The results obtained applying this new method to IRIS-classification are similar to that of other fuzzy-neural approaches, but only lesser number of rules and membership functions are necessary. This system based on RBF-neurons and extended restricted coulomb energy (RCE) learning allows very fast construction of expert knowledge only from input/output data without externally provided expert help and superfluous input features can be removed automatically after training the network. >