New estimation method based on genetic algorithm and its application to control of moving train

Seong Keun Park, Jae Phil Hwang, Kyung Jin Rou, Eun Tai Kim, Min Yong Park

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

A particle filter deals with the state estimation problem for not only linear models with Gaussian noise but also for the non-linear models with non-Gaussian noise and receives great attention from many engineering fields. In the implementation of the particle filter, a resampling scheme is used to decrease the degeneracy phenomenon and improve estimation performance. Unfortunately, however, it comes out at the cost of the undesired the particle deprivation problem. In order to overcome this problem of the particle filter, we propose a novel filtering method called the genetic filter. Then the proposed filter, we embed the genetic algorithm into the particle filter and overcome the problems of particle filter. . Finally, the genetic filter is applied to the estimation problem of a moving train and its effectiveness is illustrated through computer simulation.

Original languageEnglish
Title of host publication2006 SICE-ICASE International Joint Conference
Pages3156-3159
Number of pages4
DOIs
Publication statusPublished - 2006
Event2006 SICE-ICASE International Joint Conference - Busan, Korea, Republic of
Duration: 2006 Oct 182006 Oct 21

Publication series

Name2006 SICE-ICASE International Joint Conference

Other

Other2006 SICE-ICASE International Joint Conference
CountryKorea, Republic of
CityBusan
Period06/10/1806/10/21

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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  • Cite this

    Park, S. K., Hwang, J. P., Rou, K. J., Kim, E. T., & Park, M. Y. (2006). New estimation method based on genetic algorithm and its application to control of moving train. In 2006 SICE-ICASE International Joint Conference (pp. 3156-3159). [4108188] (2006 SICE-ICASE International Joint Conference). https://doi.org/10.1109/SICE.2006.314824