A simply identified Sugeno-type fuzzy model via double clustering

Euntai Kim, Heejin Lee, Minkee Park, Mignon Park

Research output: Contribution to journalArticle

109 Citations (Scopus)

Abstract

Recently fuzzy models have received significant attention from various fields and many researchers have conducted researches regarding them. Especially, Sugeno suggested so called the Sugeno-type fuzzy model which superbly describes a nonlinear system. In this paper, we suggest a new identification method for the Sugeno-type fuzzy model. The suggested algorithm is much simpler than the original identification strategy adopted in [1-4]. The algorithm suggested in this paper is similar to that of [5,6] in that the algorithm suggested in this paper consists of two steps: coarse tuning and fine tuning. In this paper, double clustering strategy is proposed for coarse tuning. Finally, the results of computer simulation are given to demonstrate the validity of this algorithm.

Original languageEnglish
Pages (from-to)25-39
Number of pages15
JournalInformation sciences
Volume110
Issue number1-2
DOIs
Publication statusPublished - 1998 Jan 1

Fingerprint

Fuzzy Model
Clustering
Tuning
Nonlinear systems
Identification (control systems)
Computer Simulation
Nonlinear Systems
Computer simulation
Demonstrate
Strategy

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

Cite this

Kim, Euntai ; Lee, Heejin ; Park, Minkee ; Park, Mignon. / A simply identified Sugeno-type fuzzy model via double clustering. In: Information sciences. 1998 ; Vol. 110, No. 1-2. pp. 25-39.
@article{c4c1f6458348426f9848de5579c2b362,
title = "A simply identified Sugeno-type fuzzy model via double clustering",
abstract = "Recently fuzzy models have received significant attention from various fields and many researchers have conducted researches regarding them. Especially, Sugeno suggested so called the Sugeno-type fuzzy model which superbly describes a nonlinear system. In this paper, we suggest a new identification method for the Sugeno-type fuzzy model. The suggested algorithm is much simpler than the original identification strategy adopted in [1-4]. The algorithm suggested in this paper is similar to that of [5,6] in that the algorithm suggested in this paper consists of two steps: coarse tuning and fine tuning. In this paper, double clustering strategy is proposed for coarse tuning. Finally, the results of computer simulation are given to demonstrate the validity of this algorithm.",
author = "Euntai Kim and Heejin Lee and Minkee Park and Mignon Park",
year = "1998",
month = "1",
day = "1",
doi = "10.1016/S0020-0255(97)10083-4",
language = "English",
volume = "110",
pages = "25--39",
journal = "Information Sciences",
issn = "0020-0255",
publisher = "Elsevier Inc.",
number = "1-2",

}

A simply identified Sugeno-type fuzzy model via double clustering. / Kim, Euntai; Lee, Heejin; Park, Minkee; Park, Mignon.

In: Information sciences, Vol. 110, No. 1-2, 01.01.1998, p. 25-39.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A simply identified Sugeno-type fuzzy model via double clustering

AU - Kim, Euntai

AU - Lee, Heejin

AU - Park, Minkee

AU - Park, Mignon

PY - 1998/1/1

Y1 - 1998/1/1

N2 - Recently fuzzy models have received significant attention from various fields and many researchers have conducted researches regarding them. Especially, Sugeno suggested so called the Sugeno-type fuzzy model which superbly describes a nonlinear system. In this paper, we suggest a new identification method for the Sugeno-type fuzzy model. The suggested algorithm is much simpler than the original identification strategy adopted in [1-4]. The algorithm suggested in this paper is similar to that of [5,6] in that the algorithm suggested in this paper consists of two steps: coarse tuning and fine tuning. In this paper, double clustering strategy is proposed for coarse tuning. Finally, the results of computer simulation are given to demonstrate the validity of this algorithm.

AB - Recently fuzzy models have received significant attention from various fields and many researchers have conducted researches regarding them. Especially, Sugeno suggested so called the Sugeno-type fuzzy model which superbly describes a nonlinear system. In this paper, we suggest a new identification method for the Sugeno-type fuzzy model. The suggested algorithm is much simpler than the original identification strategy adopted in [1-4]. The algorithm suggested in this paper is similar to that of [5,6] in that the algorithm suggested in this paper consists of two steps: coarse tuning and fine tuning. In this paper, double clustering strategy is proposed for coarse tuning. Finally, the results of computer simulation are given to demonstrate the validity of this algorithm.

UR - http://www.scopus.com/inward/record.url?scp=0032164985&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0032164985&partnerID=8YFLogxK

U2 - 10.1016/S0020-0255(97)10083-4

DO - 10.1016/S0020-0255(97)10083-4

M3 - Article

AN - SCOPUS:0032164985

VL - 110

SP - 25

EP - 39

JO - Information Sciences

JF - Information Sciences

SN - 0020-0255

IS - 1-2

ER -