An Experiment on the Design of Radial Basis Function Neural Networks for Software Cost Estimation
Ali Idri, Alain Abran, Samir Mbarki · 2006
This paper is concerned with the use of radial basis function (RBF) neural networks for software cost estimation. The study is devoted to the design of these networks, especially their middle layer composed of receptive fields, using two clustering techniques: the C-means and the APC-III algorithms. A comparison between an RBFN using C-means and an RBFN using APC-III, in terms of estimates accuracy, is hence presented. This study is based on the COCOMO'81 dataset