AN EFFICIENT METHOD TO CONSTRUCT A RADIAL BASIS FUNCTION NEURAL-NETWORK CLASSIFIER

Radial basis function neural network (RBFN) has the power of the unive rsal function approximation But how to construct an RBFN to solve a gi ven problem is usually not straightforward. This paper describes a met hod to construct an RBFN classifier efficiently and effectively. The m ethod determines the middle layer neurons by a fast clustering algorit hm and computes the optimal weights between the middle and the output layers statistically. We applied the proposed method to construct an R BFN classifier for an unconstrained handwritten digit recognition. The experiment showed that the method could construct an RBFN classifier quickly and the performance of the classifier was better than the best result previously reported. (C) 1997 Elsevier Science Ltd.