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Radial basis function network using intuitionistic fuzzy C means for software cost estimation

Software development has become an important activity for many modern organisations. Software engineers have become more and more concerned about accurately predicting the cost and quality of software product under development. In the last few decades many software cost estimation models have been developed but no model has proved to be successful at effectively and consistently predicting software development cost. In this paper we propose the use of Radial Basis Function Network RBFN for software cost estimation using Intuitionistic Fuzzy C Means IFCM with Gaussian potential functions. This technique selects the most desirable cluster centres, thereby increasing the clustering accuracy which results in improved software cost estimations. A comparison of RBFN using IFCM, Fuzzy C Means FCM and conventional COCOMO model is presented. The datasets used in our study are the COCOMO81 dataset and NASA93 dataset. Experimental results are given to show the effectiveness of the proposed method.