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Enhanced Time-Dependent Evolutionary Fuzzy Support Vector Machines Inference Model For Cash Flow Prediction And Estimate At Completion

Author

Listed:
  • MIN-YUAN CHENG

    (Department of Construction Engineering, National Taiwan University of Science and Technology, #43, Sec. 4, Keelung Road, Taipei 106, Taiwan, R.O.C.)

  • YU-WEI WU

    (Department of Construction Engineering, National Taiwan University of Science and Technology, #43, Sec. 4, Keelung Road, Taipei 106, Taiwan, R.O.C.)

  • LE TRUNG DAN

    (Department of Construction Engineering, National Taiwan University of Science and Technology, #43, Sec. 4, Keelung Road, Taipei 106, Taiwan, R.O.C.)

  • ANDREAS F. VAN ROY

    (Department of Civil Engineering, Parahyangan Catholic University, Jl. Ciumbuleuit 94, Bandung 40141, West Java, Indonesia)

Abstract

This study conducts a mechanism enhancing the time series data treatment of the time-dependent evolutionary fuzzy support vector machine inference model (EFSIMT). The enhanced model, EFSIMET, was developed particularly to treat construction management problems that contain time series data. EFSIMETis an artificial intelligent hybrid system in which fuzzy logic (FL) deal with vagueness and approximate reasoning; support vector machine (SVM) acts as supervise learning tool; and fast messy genetic algorithm (fmGA) works to optimize FL and SVMs parameters simultaneously. Moreover, to capture the time series data characteristics, the inference model develops fmGA-based searching mechanism to seek suitable weight values to weight the training data points. This random-based searching mechanism has capacity to address the complex and dynamic nature of time series data; thus, it could improve the model's performance significantly. Nowadays, construction managementis facing complex and difficult problems due to the increasing uncertainties during project implementation. Therefore, the second objective of this study is proposed for the application of EFSIMETto treat two typical problems in construction: forecasting cash flow and estimate at completion. Through performance's comparison with previous works, the effectiveness and reliability of EFSIMETare proven. Hence, this model may be used as an intelligent decision support tool to assist the decision-making process to solve the construction management's difficulties.

Suggested Citation

  • Min-Yuan Cheng & Yu-Wei Wu & Le Trung Dan & Andreas F. Van Roy, 2013. "Enhanced Time-Dependent Evolutionary Fuzzy Support Vector Machines Inference Model For Cash Flow Prediction And Estimate At Completion," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 12(04), pages 679-710.
    • Handle: RePEc:wsi:ijitdm:v:12:y:2013:i:04:n:s0219622013500259
    DOI: 10.1142/S0219622013500259
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    Cited by:

    1. Carlo Alberto Magni & Stefano Malagoli & Andrea Marchioni & Giovanni Mastroleo, 2020. "Rating firms and sensitivity analysis," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 71(12), pages 1940-1958, December.
    2. Cheng, Min-Yuan & Cao, Minh-Tu & Herianto, Jason Ghorman, 2020. "Symbiotic organisms search-optimized deep learning technique for mapping construction cash flow considering complexity of project," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).

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