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Comparison of the Inversion Methods for Probability Integral Parameters

  • Jingyu Yang
  • Shuai Yu
  • Chao LiuEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 980)

Abstract

The probability integral model is an important model to study the law of surface movement and deformation caused by coal mining. There are a certain number of undetermined parameters in the model, which need to be inverted by a certain mathematical method. In this paper, the objective function is established by the probability integral method, and the parameter inversion in the probability integral method is carried out by using the normal parameter inversion methods such as pattern search method, genetic algorithm and particle swarm optimization. By contrast the inversion results, the advantages and disadvantages of each algorithm in the stability of parameter results, resistance to gross errors and missing observation points are proved. The results show that PSM, GA and PSO are feasible to inverse parameters in the probability integral method, and the results are stable in the presence of gross errors and observation points. Compared with PSM and GA, the inversion accuracy of PSO is improved to some extent, which shows the advantages of PSO in parameter inversion of the probability integral method.

Keywords

Probability integral method Parameters inversion Pattern search method Genetic algorithm Particle swarm optimization 

Notes

Acknowledgments

The project was financially supported by the National Natural Science Foundation of China (no. 41404004).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of GeomaticsAnhui University of Science and TechnolgyHuainanPeople’s Republic of China
  2. 2.Fuyang City Survey Institute in AnhuiFuyangPeople’s Republic of China

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