Hybrid Optimization Scheme for Radial Basis Function Neural Network

Abstract

Radial Basis Function Neural Network (RBFNN) is a curve fitting tool in a higher dimensional space. The nature of this surface depends mainly on the number of neurons in the hidden layer. The number of hidden neurons is decided by the number of clusters into which the data-set gets divided. It has been shown that the accuracy in prediction depends upon the quality of the clusters. To obtain good quality clusters, in this study, a hybrid optimization scheme of running a genetic algorithm in the outer loop, while simultaneously running a back-propagation algorithm in the inner loop, has been adopted. The number of hidden neurons is kept the same with that of clusters formed by an algorithm proposed here, apart from the popular fuzzy-c-means and entropy-based clustering algorithms. RBFNN developed using the proposed clustering algorithm is found to perform better than that obtained utilizing the other two clustering algorithms. The method has been successfully implemented in both forward and reverse mappings of electron beam welding process.