Efficient neuro-fuzzy control systems for autonomous underwater vehicle control

J. S. Wang, C. S.G. Lee

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

19 Citations (Scopus)

Abstract

This paper examines several clustering methods for the structure learning in constructing efficient neuro-fuzzy systems. The structure learning establishes the internal structure (i.e., the number of term sets and fuzzy-rule base generation) of a given neuro-fuzzy architecture. The fundamental ideas of existing rule generation algorithms are addressed and discussed. Performance of the neuro-fuzzy systems established from these clustering methods is validated through computer simulations of the classification problem of IRIS and the control example of an autonomous underwater vehicle.

Original languageEnglish
Title of host publicationProceedings - IEEE International Conference on Robotics and Automation
Pages2986-2991
Number of pages6
DOIs
Publication statusPublished - 2001 Sep 15
Event2001 IEEE International Conference on Robotics and Automation - Seoul, Korea, Republic of
Duration: 2001 May 212001 May 26

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
Volume3
ISSN (Print)1050-4729

Other

Other2001 IEEE International Conference on Robotics and Automation
CountryKorea, Republic of
CitySeoul
Period01-05-2101-05-26

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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