A new approach to the identification of a fuzzy model

Minkee Park, Seunghwan Ji, Euntai Kim, Mignon Park

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

This paper presents an approach which is useful for the identification of a fuzzy model. The identification of a fuzzy model using input-output data consists of two parts: structure identification and parameter identification. In this paper, algorithms to identify those parameters and structures are suggested to solve the problems of conventional methods. Given a set of input-output data, the consequent parameters are identified by the Hough transform and clustering method, which consider the linearity and continuity, respectively. For the premise part identification, the input space is partitioned by a clustering method. The gradient descent algorithm is used to fine-tune parameters of a fuzzy model. Finally, it is shown that this method is useful for the identification of a fuzzy model by simulation.

Original languageEnglish
Pages (from-to)169-181
Number of pages13
JournalFuzzy Sets and Systems
Volume104
Issue number2
DOIs
Publication statusPublished - 1999 Jun 1

Fingerprint

Fuzzy Model
Identification (control systems)
Clustering Methods
Structure Identification
Hough Transform
Descent Algorithm
Output
Gradient Algorithm
Gradient Descent
Parameter Identification
Linearity
Hough transforms
Simulation

All Science Journal Classification (ASJC) codes

  • Logic
  • Artificial Intelligence

Cite this

Park, Minkee ; Ji, Seunghwan ; Kim, Euntai ; Park, Mignon. / A new approach to the identification of a fuzzy model. In: Fuzzy Sets and Systems. 1999 ; Vol. 104, No. 2. pp. 169-181.
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A new approach to the identification of a fuzzy model. / Park, Minkee; Ji, Seunghwan; Kim, Euntai; Park, Mignon.

In: Fuzzy Sets and Systems, Vol. 104, No. 2, 01.06.1999, p. 169-181.

Research output: Contribution to journalArticle

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