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Software Effort Prediction Using Fuzzy Clustering and Functional Link Artificial Neural Networks

  • Tirimula Rao Benala
  • Rajib Mall
  • Satchidananda Dehuri
  • V. L. Prasanthi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7677)

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.

Keywords

Software cost estimation Fuzzy C-Means K-Means FLANN 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Tirimula Rao Benala
    • 1
  • Rajib Mall
    • 2
  • Satchidananda Dehuri
    • 3
  • V. L. Prasanthi
    • 1
  1. 1.Department of Computer Science and EngineeringAnil Neerukonda Institute of Technology and SciencesVisakhapatnamIndia
  2. 2.Department of Computer Science and EngineeringIndian Institute of TechnologyKharagpurIndia
  3. 3.Department of System EngineeringAjou UniversityYeongtong-guSouth Korea

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