Multi-variables singular value based rule interpolation

P. Baranyi, Yeung Yam, L. Kóczy

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

2 Citations (Scopus)

Abstract

Fuzzy interpolative techniques have emerged recently as a new topic of fuzzy theories. The main advantage of fuzzy rule interpolation is that, unlike classical methods, it can function with sparse rule base, thereby increasing the applicability of fuzzy reasoning. A major difficulty of fuzzy reasoning is that the size of rule bases increases exponentially with the number of variables or the number of fuzzy terms, and hince also the inference/control time. Interpolative reasoning can help to reduce the number of rules using sparse rule base, but does not eliminate the problem of exponentially growing. Singular value based rule base reduction (FuzzySVD) methods have been published to various conventional methods. The interpolation technique specialized for full rule base combines the advantageous of fuzzy rule interpolation and classical methods. This paper introduces the extension of the FuzzySVD method to the specialized fuzzy rule interpolation method to achieve more significant reduction. This method is an extension of the two variables method to multi variables.

Original languageEnglish
Title of host publicationProceedings of the IEEE International Conference on Systems, Man and Cybernetics
PublisherIEEE
Pages1598-1603
Number of pages6
Volume2
Publication statusPublished - 1997
EventProceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics. Part 3 (of 5) - Orlando, FL, USA
Duration: Oct 12 1997Oct 15 1997

Other

OtherProceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics. Part 3 (of 5)
CityOrlando, FL, USA
Period10/12/9710/15/97

Fingerprint

Interpolation
Fuzzy rules

ASJC Scopus subject areas

  • Hardware and Architecture
  • Control and Systems Engineering

Cite this

Baranyi, P., Yam, Y., & Kóczy, L. (1997). Multi-variables singular value based rule interpolation. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (Vol. 2, pp. 1598-1603). IEEE.

Multi-variables singular value based rule interpolation. / Baranyi, P.; Yam, Yeung; Kóczy, L.

Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. Vol. 2 IEEE, 1997. p. 1598-1603.

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

Baranyi, P, Yam, Y & Kóczy, L 1997, Multi-variables singular value based rule interpolation. in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. vol. 2, IEEE, pp. 1598-1603, Proceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics. Part 3 (of 5), Orlando, FL, USA, 10/12/97.
Baranyi P, Yam Y, Kóczy L. Multi-variables singular value based rule interpolation. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. Vol. 2. IEEE. 1997. p. 1598-1603
Baranyi, P. ; Yam, Yeung ; Kóczy, L. / Multi-variables singular value based rule interpolation. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. Vol. 2 IEEE, 1997. pp. 1598-1603
@inproceedings{de3fd3e9c0214b198cbd89e76c93f450,
title = "Multi-variables singular value based rule interpolation",
abstract = "Fuzzy interpolative techniques have emerged recently as a new topic of fuzzy theories. The main advantage of fuzzy rule interpolation is that, unlike classical methods, it can function with sparse rule base, thereby increasing the applicability of fuzzy reasoning. A major difficulty of fuzzy reasoning is that the size of rule bases increases exponentially with the number of variables or the number of fuzzy terms, and hince also the inference/control time. Interpolative reasoning can help to reduce the number of rules using sparse rule base, but does not eliminate the problem of exponentially growing. Singular value based rule base reduction (FuzzySVD) methods have been published to various conventional methods. The interpolation technique specialized for full rule base combines the advantageous of fuzzy rule interpolation and classical methods. This paper introduces the extension of the FuzzySVD method to the specialized fuzzy rule interpolation method to achieve more significant reduction. This method is an extension of the two variables method to multi variables.",
author = "P. Baranyi and Yeung Yam and L. K{\'o}czy",
year = "1997",
language = "English",
volume = "2",
pages = "1598--1603",
booktitle = "Proceedings of the IEEE International Conference on Systems, Man and Cybernetics",
publisher = "IEEE",

}

TY - GEN

T1 - Multi-variables singular value based rule interpolation

AU - Baranyi, P.

AU - Yam, Yeung

AU - Kóczy, L.

PY - 1997

Y1 - 1997

N2 - Fuzzy interpolative techniques have emerged recently as a new topic of fuzzy theories. The main advantage of fuzzy rule interpolation is that, unlike classical methods, it can function with sparse rule base, thereby increasing the applicability of fuzzy reasoning. A major difficulty of fuzzy reasoning is that the size of rule bases increases exponentially with the number of variables or the number of fuzzy terms, and hince also the inference/control time. Interpolative reasoning can help to reduce the number of rules using sparse rule base, but does not eliminate the problem of exponentially growing. Singular value based rule base reduction (FuzzySVD) methods have been published to various conventional methods. The interpolation technique specialized for full rule base combines the advantageous of fuzzy rule interpolation and classical methods. This paper introduces the extension of the FuzzySVD method to the specialized fuzzy rule interpolation method to achieve more significant reduction. This method is an extension of the two variables method to multi variables.

AB - Fuzzy interpolative techniques have emerged recently as a new topic of fuzzy theories. The main advantage of fuzzy rule interpolation is that, unlike classical methods, it can function with sparse rule base, thereby increasing the applicability of fuzzy reasoning. A major difficulty of fuzzy reasoning is that the size of rule bases increases exponentially with the number of variables or the number of fuzzy terms, and hince also the inference/control time. Interpolative reasoning can help to reduce the number of rules using sparse rule base, but does not eliminate the problem of exponentially growing. Singular value based rule base reduction (FuzzySVD) methods have been published to various conventional methods. The interpolation technique specialized for full rule base combines the advantageous of fuzzy rule interpolation and classical methods. This paper introduces the extension of the FuzzySVD method to the specialized fuzzy rule interpolation method to achieve more significant reduction. This method is an extension of the two variables method to multi variables.

UR - http://www.scopus.com/inward/record.url?scp=0031377447&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0031377447&partnerID=8YFLogxK

M3 - Conference contribution

VL - 2

SP - 1598

EP - 1603

BT - Proceedings of the IEEE International Conference on Systems, Man and Cybernetics

PB - IEEE

ER -