Software cost estimation models using Radial Basis Function Neural Networks

Radial Basis Function Neural Networks (RBFN) have been recently studied due to their qualification as an universal function approximation. This paper investigates the use of RBF neural networks for software cost estimation, The focus of this study is on 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 a RBFN using C-means and a RBFN using APC-III, in terms of estimates accuracy, is hence presented. This study uses the COCOMO'81 dataset and data on Web applications from the Tukutuku database.