An evolutionary approach to identification problems with incomplete output data

Joe Imae, Yasuhiko Morita, Guisheng Zhai, Tomoaki Kobayashi

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

Abstract

In this paper, we consider nonlinear system identification problems in the case where output data is incomplete. We propose an identification method based on an evolutionary algorithm, which is a fusion of a genetic algorithm (GA) and genetic programming (GP), and illustrate the effectiveness of the proposed method through a simulation and an experiment with a cart.

Original languageEnglish
Title of host publicationProceedings of SICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology
Pages2262-2265
Number of pages4
DOIs
Publication statusPublished - 2008 Dec 1
EventSICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology - Tokyo, Japan
Duration: 2008 Aug 202008 Aug 22

Publication series

NameProceedings of the SICE Annual Conference

Conference

ConferenceSICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology
CountryJapan
CityTokyo
Period08/8/2008/8/22

Keywords

  • Evolutionary computation
  • Genetic algorithm
  • Genetic programming
  • Incomplete output data
  • Nonlinear system identification

ASJC Scopus subject areas

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

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  • Cite this

    Imae, J., Morita, Y., Zhai, G., & Kobayashi, T. (2008). An evolutionary approach to identification problems with incomplete output data. In Proceedings of SICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology (pp. 2262-2265). [4655041] (Proceedings of the SICE Annual Conference). https://doi.org/10.1109/SICE.2008.4655041