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A Particle Swarm Optimized Functional Link Artificial Neural Network (PSO-FLANN) in Software Cost Estimation

  • Tirimula Rao Benala
  • Korada Chinnababu
  • Rajib Mall
  • Satchidananda Dehuri
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 199)

Abstract

We use particle swarm optimization (PSO) to train the functional link artificial neural network (FLANN) for software effort prediction. The combined framework is known as PSO-FLANN. This framework exploits the global classification capability of PSO and FLANN’s complex nonlinear mapping between its input and output pattern space by using functional expansion. The Chebyshev polynomial has been used as choice of expansion in FLANN to exhaustively study the performance in three real time datasets. The simulation results show that it not only deals efficiently with noisy data but achieves improved accuracy in prediction.

Keywords

Software cost estimation Particle Swarm optimization Functional Link Artificial Neural Networks 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Tirimula Rao Benala
    • 1
  • Korada Chinnababu
    • 1
  • Rajib Mall
    • 2
  • Satchidananda Dehuri
    • 3
  1. 1.Anil Neerukonda Institute of Technology and SciencesVisakhapatnamIndia
  2. 2.Department of Computer Science and EngineeringIndian Institute of TechnologyKharagpurIndia
  3. 3.Department of Systems EngineeringAjou UniversityYeongtong-guRepublic of Korea

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