SVD-based fuzzy model reduction strategy

J. Yen, L. Wang

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

23 Scopus citations

Abstract

This paper describes a novel fuzzy model reduction approach for overcoming the curse of dimensionality associated with high-dimensional data modeling problems. A numerically reliable orthogonal transformation technique, known as the singular value decomposition (SVD), is utilized to detect and select the dominant fuzzy rules from a rule base. The effectiveness of the proposed approach is illustrated using a nonlinear limit cycle modeling problem.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Fuzzy Systems
Editors Anon
PublisherIEEE
Pages835-841
Number of pages7
Volume2
StatePublished - 1996
EventProceedings of the 1996 5th IEEE International Conference on Fuzzy Systems. Part 3 (of 3) - New Orleans, LA, USA
Duration: Sep 8 1996Sep 11 1996

Other

OtherProceedings of the 1996 5th IEEE International Conference on Fuzzy Systems. Part 3 (of 3)
CityNew Orleans, LA, USA
Period9/8/969/11/96

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Software
  • Artificial Intelligence
  • Applied Mathematics

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  • Cite this

    Yen, J., & Wang, L. (1996). SVD-based fuzzy model reduction strategy. In Anon (Ed.), IEEE International Conference on Fuzzy Systems (Vol. 2, pp. 835-841). IEEE.