A clustering algorithm for implementation of RBF technique

Summary form only given, as follows. An approach to the implementation of the radial basis function (RBF) learning paradigm was developed and applied to neural network of the appropriate architecture. The authors explored the extent to which the number of RBF nodes can be reduced without significantly affecting the overall training error. This is accomplished through an effective clustering algorithm. Emphasis is also placed on the problems faced by a technique that has been proved superior to the more traditional training algorithms, particularly in terms of processing speed and solvability of nonlinear patterns. Solutions were consequently proposed for making the RBF a more efficient method for interpolation and classification purposes.<
>