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Software Effort Estimation Using Functional Link Neural Networks Optimized by Improved Particle Swarm Optimization

  • Tirimula Rao Benala
  • Rajib Mall
  • Satchidananda Dehuri
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8298)

Abstract

This paper puts forward a new learning model based on improved particle swarm optimization (ISO) for functional link artificial neural networks (FLANN) to estimate software effort. The improved PSO uses the adaptive inertia to balance the tradeoff between exploration and exploitation of the search space while training FLANN. The Chebyshev polynomial has been used for mapping the original feature space from lower to higher dimensional functional space. The method has been evaluated exhaustively on different test suits of PROMISE repository to study the performance. The simulation results show that the ISO learning algorithm greatly improves the performance of FLANN and its variants for software development effort estimation.

Keywords

Software effort estimation ISO Back propagation FLANN 

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Tirimula Rao Benala
    • 1
  • Rajib Mall
    • 2
  • Satchidananda Dehuri
    • 3
  1. 1.Department of Information TechnologyJawaharlal Nehru Technological University Kakinada, University College Of EngineeringVizianagaramIndia
  2. 2.Department of Computer Science and EngineeringIndian Institute of TechnologyKharagpurIndia
  3. 3.Department of System EngineeringAjou UniversitySuwonSouth Korea

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