Adaptation of SVD-based fuzzy reduction via minimal expansion

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Most adopted fuzzy inference techniques do not hold the universal approximation property if the numbers of antecedent sets are limited. This fact and the exponential complexity problem of widely adopted fuzzy logic techniques show the contradictory features of fuzzy rule bases in pursuit of good approximation. As a result, complexity reduction emerged in fuzzy theory. The natural disadvantage of using complexity reduction is that the adaptivity property of the reduced approximation becomes highly restricted. This paper proposes a technique for singular value decomposition (SVD) based reduction developed in [1], which may alleviate the adaptivity restriction.

Original languageEnglish
Pages (from-to)222-226
Number of pages5
JournalIEEE Transactions on Instrumentation and Measurement
Volume51
Issue number2
DOIs
Publication statusPublished - Apr 2002

Fingerprint

Singular value decomposition
decomposition
expansion
approximation
Fuzzy inference
Fuzzy rules
inference
Fuzzy logic
logic
constrictions

Keywords

  • Higher-order tensor decomposition
  • Rule-base complexity reduction
  • Singular value decomposition

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Instrumentation

Cite this

Adaptation of SVD-based fuzzy reduction via minimal expansion. / Baranyi, P.; Várkonyi-Kóczy, A.

In: IEEE Transactions on Instrumentation and Measurement, Vol. 51, No. 2, 04.2002, p. 222-226.

Research output: Contribution to journalArticle

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