Meta-Learning Evolutionary Artificial Neural Network for Selecting Flexible Manufacturing Systems

  • Arijit Bhattacharya
  • Ajith Abraham
  • Crina Grosan
  • Pandian Vasant
  • Sangyong Han
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

Abstract

This paper proposes the application of Meta-Learning Evolutionary Artificial Neural Network (MLEANN) in selecting flexible manufacturing systems (FMS) from a group of candidate FMS’s. First, multi-criteria decisionmaking (MCDM) methodology using an improved S-shaped membership function has been developed for finding out the ‘best candidate FMS alternative’ from a set of candidate-FMSs. The MCDM model trade-offs among various parameters, namely, design parameters, economic considerations, etc., affecting the FMS selection process in multi-criteria decision-making environment. Genetic algorithm is used to evolve the architecture and weights of the proposed neural network method. Further, a back-propagation (BP) algorithm is used as the local search algorithm. The selection of FMS is made according to the error output of the results found from the MCDM model.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Arijit Bhattacharya
    • 1
  • Ajith Abraham
    • 2
  • Crina Grosan
    • 4
  • Pandian Vasant
    • 3
  • Sangyong Han
    • 2
  1. 1.The Patent OfficeKolkataIndia
  2. 2.School of Computer Science and EngineeringChung-Ang UniversitySeoulKorea
  3. 3.Universiti Teknologi PetronasTronoh, BSIMalaysia
  4. 4.Department of Computer ScienceBabes-Bolyai UniversityRomania

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