Adaptation of TS fuzzy models without complexity expansion: HOSVD-based approach

P. Baranyi, A. Várkonyi-Kóczy, Yeung Yam, Ron J. Patton

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

One direction of measured data-set based modeling applies fuzzy logic identification tools and results in a fuzzy rule-base model. A typical problem of fuzzy identification methods is that the complexity of the resulting fuzzy rule-base, namely the number of rules in the rule-base, explodes with the modeling accuracy. As a result, the topic of fuzzy rule-base complexity reduction techniques emerged in the last decade. A common disadvantage of fuzzy rule-base complexity reduction methods is that the resulting complexity minimized fuzzy-rule bases cannot be simply adapted to new information. If we have new information that cannot be described by the fuzzy rules of the complexity minimized fuzzy rule-base, then we have two choices. The first choice is to add new fuzzy rules to the fuzzy rule-base until the new information can be described. The second choice is to modify the new information until it can be described by the fuzzy rule-base without using additional fuzzy rules. This second case has the prominent role if the number of fuzzy rules in the fuzzy rule-base is limited. This paper proposes a method for the second choice. The proposed method minimizes the necessary modification of the new information. This paper focuses attention on a recent complexity reduction method, termed Higher Order Singular Value Decomposition (HOSVD)-based complexity reduction, and Takagi-Sugeno (TS) inference operator-based fuzzy rule-bases. An example is used to provide the validation of the proposed method. In order to demonstrate the effectiveness of the proposed method, a control system of a differential-steered automatic guided vehicle is modeled in the paper.

Original languageEnglish
Pages (from-to)52-60
Number of pages9
JournalIEEE Transactions on Instrumentation and Measurement
Volume54
Issue number1
DOIs
Publication statusPublished - Jan 2005

Fingerprint

Fuzzy rules
Singular value decomposition
decomposition
expansion
Automatic guided vehicles
Fuzzy logic
inference
Identification (control systems)
logic

Keywords

  • Complexity reduction
  • Higher-order singular value decomposition
  • Takagi-Sugeno (TS) fuzzy model

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Instrumentation

Cite this

Adaptation of TS fuzzy models without complexity expansion : HOSVD-based approach. / Baranyi, P.; Várkonyi-Kóczy, A.; Yam, Yeung; Patton, Ron J.

In: IEEE Transactions on Instrumentation and Measurement, Vol. 54, No. 1, 01.2005, p. 52-60.

Research output: Contribution to journalArticle

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