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Global sensitivity analysis using sparse high dimensional model representations generated by the group method of data handling

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  • Lambert, Romain S.C.
  • Lemke, Frank
  • Kucherenko, Sergei S.
  • Song, Shufang
  • Shah, Nilay

Abstract

In this paper, the parameter selection capabilities of the group method of data handling (GMDH) as an inductive self-organizing modelling method are used to construct sparse random sampling high dimensional model representations (RS-HDMR), from which the Sobol’s first and second order global sensitivity indices can be derived. The proposed method is capable of dealing with high-dimensional problems without the prior use of a screening technique and can perform with a relatively limited number of function evaluations, even in the case of under-determined modelling problems. Four classical benchmark test functions are used for the evaluation of the proposed technique.

Suggested Citation

  • Lambert, Romain S.C. & Lemke, Frank & Kucherenko, Sergei S. & Song, Shufang & Shah, Nilay, 2016. "Global sensitivity analysis using sparse high dimensional model representations generated by the group method of data handling," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 128(C), pages 42-54.
    • Handle: RePEc:eee:matcom:v:128:y:2016:i:c:p:42-54
    DOI: 10.1016/j.matcom.2016.04.005
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    References listed on IDEAS

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    8. Kucherenko, Sergei & Feil, Balazs & Shah, Nilay & Mauntz, Wolfgang, 2011. "The identification of model effective dimensions using global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 96(4), pages 440-449.
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    Cited by:

    1. Baraka Mathew Nkurlu & Chuanbo Shen & Solomon Asante-Okyere & Alvin K. Mulashani & Jacqueline Chungu & Liang Wang, 2020. "Prediction of Permeability Using Group Method of Data Handling (GMDH) Neural Network from Well Log Data," Energies, MDPI, vol. 13(3), pages 1-18, January.
    2. Spiessl, Sabine M. & Kucherenko, Sergei & Becker, Dirk-A. & Zaccheus, Oluyemi, 2019. "Higher-order sensitivity analysis of a final repository model with discontinuous behaviour using the RS-HDMR meta-modeling approach," Reliability Engineering and System Safety, Elsevier, vol. 187(C), pages 149-158.
    3. Cheng, Kai & Lu, Zhenzhou, 2019. "Time-variant reliability analysis based on high dimensional model representation," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 310-319.
    4. Chongshi Gu & Xiao Fu & Chenfei Shao & Zhongwen Shi & Huaizhi Su, 2020. "Application of Spatiotemporal Hybrid Model of Deformation in Safety Monitoring of High Arch Dams: A Case Study," IJERPH, MDPI, vol. 17(1), pages 1-25, January.

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