A global learning algorithm for a RBF network

This article presents a new learning algorithm for the construction and tra ining of a RBF neural network. The algorithm is based on a global mechanism of parameter learning using a maximum likelihood classification approach. The resulting neurons in the RBF network partitions a multidimensional patt ern space into a set of maximum-size hyper-ellipsoid subspaces in terms of the statistical distributions of the training samples. An important feature of the algorithm is that the learning process includes both the tasks of d iscovering a suitable network structure and of determining the connection w eights. The entire network and its parameters are thought of evolved gradua lly in the learning process. (C) 1999 Elsevier Science Ltd. All rights rese rved.