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A learning vector quantization neural network model for the classification of industrial construction projects

Author

Listed:
  • Gupta, V. K.
  • Chen, J. G.
  • Murtaza, M. B.

Abstract

In several key functional areas of contemporary engineering and management science, neural networks have steadily been gaining recognition as robust and reliable tools for classification problems. This paper describes a new application of the learning vector quantization neural network: the classification of the degree of modularization appropriate for the construction of an industrial facility. This neural network uses variables related to plant location, labor issues, organizational issues, plant characteristics, project risks, and environmental issues as inputs to perform the classification. The neural network training and performance evaluation is also discussed.

Suggested Citation

  • Gupta, V. K. & Chen, J. G. & Murtaza, M. B., 1997. "A learning vector quantization neural network model for the classification of industrial construction projects," Omega, Elsevier, vol. 25(6), pages 715-727, December.
    • Handle: RePEc:eee:jomega:v:25:y:1997:i:6:p:715-727
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    References listed on IDEAS

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    1. Chen, S. K. & Mangiameli, P. & West, D., 1995. "The comparative ability of self-organizing neural networks to define cluster structure," Omega, Elsevier, vol. 23(3), pages 271-279, June.
    2. Kar Yan Tam & Melody Y. Kiang, 1992. "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions," Management Science, INFORMS, vol. 38(7), pages 926-947, July.
    3. Vipul K. Gupta & Deborah J. Fisher & Mirza B. Murtaza, 1996. "A Consortium Sponsored Knowledge-Based System for Managerial Decision Making in Industrial Construction," Interfaces, INFORMS, vol. 26(6), pages 9-23, December.
    4. Chiang, W. -C. & Urban, T. L. & Baldridge, G. W., 1996. "A neural network approach to mutual fund net asset value forecasting," Omega, Elsevier, vol. 24(2), pages 205-215, April.
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