Development of a self-organized neuro-fuzzy model by using genetic algorithm for system identification

Chuen Jyh Chen, Shih Ming Yang, Shih Guei Lin

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

In neuro-fuzzy applications, it is known that the selections in neural network structure, fuzzy logic membership functions, and fuzzy logic rules are very challenging as they are sensitive to modeling accuracy. A neuro-fuzzy model with genetic algorithm is developed for system identification, where fuzzy logic is to tune the membership functions by three-phase learning and genetic algorithm is to search the optimal parameters of the model. The weight/bias in artificial neural network, the center/width of membership function, and the fuzzy logic rules can all be determined. Performance verification of system identification by a benchmark nonlinear difference equation shows that the neuro-fuzzy model with genetic algorithm is most effective in modeling accuracy.

Original languageEnglish
Pages (from-to)281-290
Number of pages10
JournalJournal of Aeronautics, Astronautics and Aviation
Volume46
Issue number4
DOIs
Publication statusPublished - 2014 Dec 1

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Space and Planetary Science

Fingerprint Dive into the research topics of 'Development of a self-organized neuro-fuzzy model by using genetic algorithm for system identification'. Together they form a unique fingerprint.

Cite this