A two-pass hybrid training algorithm for RBF networks

Abstract

This paper presents a systematic construction of linearly weighted Gaussian radial basis function (RBF) neural network. The proposed method is computationally a two-stage hybrid training algorithm. The first stage of the hybrid algorithm is a pre-processing unit which generates a coarsely-tuned RBF network. The second stage is a fine-tuning phase. The coarsely-tuned RBF network is then optimized by using a two-pass training algorithm. In forward-pass, the output weights of RBF are calculated by the Levenberg - Marquardt (LM) algorithm while the rest of the parameters is remained fixed. Similarly, in backward-pass, the free parameters of basis function (center and width of each node) are adjusted by gradient descent (GD) algorithm while the output weights of RBF are remained fixed. Hence, the effectiveness of the proposed method for an RBF network is demonstrated with simulations.