Software effort prediction using fuzzy clustering and functional link artificial neural networks

Authors:
Tirimula Rao Benala;Rajib Mall;Satchidananda Dehuri;V. L. Prasanthi
Affiliations:
Department of Computer Science and Engineering, Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, Andhra Pradesh, India;Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur, India;Department of System Engineering, Ajou University, Yeongtong-gu, Suwon, South Korea;Department of Computer Science and Engineering, Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, Andhra Pradesh, India
Venue:
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
Year:
2012

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Abstract

We use the combined fuzzy C-Means (FCM) clustering algorithm and functional link artificial neural networks (FLANN) to achieve accurate software effort prediction. FLANN is a computationally efficient nonlinear network and is capable for complex nonlinear mapping between its input and output pattern space. The nonlinearity is introduced into the FLANN by passing the input pattern through a functional expansion unit. The proposed method uses three real time datasets. The Chebyshev polynomial has been used as choice of expansion to exhaustively study the performance. The simulation results show that it not only deals efficiently with noisy data but also proves to be a champion in producing promising results.