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A Method of Parameter Calibration with Hybrid Uncertainty

  • Liu Bo
  • Shang XiaoBing
  • Wang Songyan
  • Chao TaoEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 946)

Abstract

A method, which combines the cumulative distribution function and modified Kolmogorov–Smirnov test, is proposed to solve parameter calibration problem with genetic algorithm seeking the optimal result, due to the hybrid uncertainty in model. The framework is built on comparing the difference between cumulative distribution functions of some target observed values and that of sample values. First, an auxiliary variable method is used to decomposition hybrid parameters into sub-parameters with only one kind of uncertainty, which is aleatory or epistemic, because only epistemic uncertainty can be calibrated. Then we find optimal matching values with genetic algorithm according to the index of difference of joint cumulative distribution functions. Finally, we demonstrate that the proposed model calibration method is able to get the approximation values of the unknown true value of epistemic parameters, in mars entry dynamics profile. The example illustrates the rationality and efficiency of the method of this paper.

Keywords

Hybrid uncertainty Parameter calibration Auxiliary variable 

Notes

Acknowledgement

This work was supported by National Natural Science Foundation of China (No. 61790562, 61627810, 61403096).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Liu Bo
    • 1
    • 2
  • Shang XiaoBing
    • 1
  • Wang Songyan
    • 1
  • Chao Tao
    • 1
    Email author
  1. 1.Harbin Institute of TechnologyHarbinChina
  2. 2.China Shipbuilding Industry CorporationXi’anChina

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