Adaptation of SVD-based fuzzy reduction via minimal expansion

Author

Baranyi, Péter ; Várkonyi-Kóczy, Annamária R.

Author_Institution

Integrated Intelligent Syst. Japanese-Hungarian Lab., Budapest Univ. of Technol. & Econ., Hungary

Volume

51

Issue

2

fYear

2002

fDate

4/1/2002 12:00:00 AM

Firstpage

222

Lastpage

226

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 the singular value decomposition (SVD) based reduction developed by Yam et al. (see IEEE Trans. Fuzzy Syst., vol. 7, p. 120-131, Feb. 1999), which may alleviate the adaptivity restriction

Keywords

computational complexity; fuzzy logic; fuzzy set theory; inference mechanisms; singular value decomposition; SVD-based fuzzy reduction; adaptivity property; fuzzy inference techniques; fuzzy rule bases; higher-order tensor decomposition; minimal expansion; rule-based complexity reduction; singular value decomposition; Approximation error; Costs; Fuzzy logic; Fuzzy sets; Intelligent systems; Linear algebra; Scholarships; Singular value decomposition; Tensile stress;

fLanguage

English

Journal_Title

Instrumentation and Measurement, IEEE Transactions on

Publisher

ieee

ISSN

0018-9456

Type

jour

DOI

10.1109/19.997816

Filename

997816