System identification by neuro-fuzzy model with sugeno and mamdani fuzzy rules

Chuen Jyh Chen, Shih-Ming Yang, Zi Cheng Wung

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

It has been known that fuzzy system provides a framework to handle uncertainties and vagueness, but the applications often face difficulties in deciding the number of inference rules, either in Sugeno or Mamdani fuzzy rules, and input/output membership functions. A five-layer neuro-fuzzy model is developed in this work and its model accuracy is validated by a nonlinear model benchmark test. Analysis shows that both Sugeno and Mamdani neuro-fuzzy models have good performance in system identification, and the former achieves better accuracy.

Original languageEnglish
Pages (from-to)263-270
Number of pages8
JournalJournal of Aeronautics, Astronautics and Aviation
Volume41
Issue number4
Publication statusPublished - 2009 Jan 1

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Space and Planetary Science

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