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A Self-Adaptive Fuzzy Inference Model Based on Least Squares SVM for Estimating Compressive Strength of Rubberized Concrete

Author

Listed:
  • Min-Yuan Cheng

    (Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, #43, Section 4, Keelung Road, Daan District, Taipei 106, Taiwan)

  • Nhat-Duc Hoang

    (Institute of Research and Development, Faculty of Civil Engineering, Duy Tan University, P809 - K7/25 Quang Trung, Danang 55000, Vietnam)

Abstract

This paper presents an AI approach named as self-Adaptive fuzzy least squares support vector machines inference model (SFLSIM) for predicting compressive strength of rubberized concrete. The SFLSIM consists of a fuzzification process for converting crisp input data into membership grades and an inference engine which is constructed based on least squares support vector machines (LS-SVM). Moreover, the proposed inference model integrates differential evolution (DE) to adaptively search for the most appropriate profiles of fuzzy membership functions (MFs) as well as the LS-SVM’s tuning parameters. In this study, 70 concrete mix samples are utilized to train and test the SFLSIM. According to experimental results, the SFLSIM can achieve a comparatively low MAPE which is less than 2%.

Suggested Citation

  • Min-Yuan Cheng & Nhat-Duc Hoang, 2016. "A Self-Adaptive Fuzzy Inference Model Based on Least Squares SVM for Estimating Compressive Strength of Rubberized Concrete," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(03), pages 603-619, May.
    • Handle: RePEc:wsi:ijitdm:v:15:y:2016:i:03:n:s0219622016500140
    DOI: 10.1142/S0219622016500140
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    References listed on IDEAS

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