SVD-based fuzzy model reduction strategy

J. Yen; L. Wang

SVD-based fuzzy model reduction strategy

J. Yen
, L. Wang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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 language	English (US)
Title of host publication	IEEE International Conference on Fuzzy Systems
Editors	Anon
Publisher	IEEE
Pages	835-841
Number of pages	7
Volume	2
State	Published - 1996
Event	Proceedings of the 1996 5th IEEE International Conference on Fuzzy Systems. Part 3 (of 3) - New Orleans, LA, USA Duration: Sep 8 1996 → Sep 11 1996

Other

Other	Proceedings of the 1996 5th IEEE International Conference on Fuzzy Systems. Part 3 (of 3)
City	New Orleans, LA, USA
Period	9/8/96 → 9/11/96

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Software
Artificial Intelligence
Applied Mathematics

Cite this

@inproceedings{b851f5fcb56c40b59f8ebb74d97fc759,

title = "SVD-based fuzzy model reduction strategy",

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.",

author = "J. Yen and L. Wang",

year = "1996",

language = "English (US)",

volume = "2",

pages = "835--841",

editor = "Anon",

booktitle = "IEEE International Conference on Fuzzy Systems",

publisher = "IEEE",

note = "Proceedings of the 1996 5th IEEE International Conference on Fuzzy Systems. Part 3 (of 3) ; Conference date: 08-09-1996 Through 11-09-1996",

}

TY - GEN

T1 - SVD-based fuzzy model reduction strategy

AU - Yen, J.

AU - Wang, L.

PY - 1996

Y1 - 1996

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/0030409029

UR - https://www.scopus.com/pages/publications/0030409029#tab=citedBy

M3 - Conference contribution

AN - SCOPUS:0030409029

VL - 2

SP - 835

EP - 841

BT - IEEE International Conference on Fuzzy Systems

A2 - Anon, null

PB - IEEE

T2 - Proceedings of the 1996 5th IEEE International Conference on Fuzzy Systems. Part 3 (of 3)

Y2 - 8 September 1996 through 11 September 1996

ER -

SVD-based fuzzy model reduction strategy

Abstract

Other

All Science Journal Classification (ASJC) codes

Other files and links

Fingerprint

Cite this