A simply identified Sugeno-type fuzzy model via double clustering

Euntai Kim, Heejin Lee, Minkee Park, Mignon Park

Research output: Contribution to journalArticlepeer-review

112 Citations (Scopus)


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
Issue number1-2
Publication statusPublished - 1998 Sept

Bibliographical note

Funding Information:
*Corresponding author. Tel.: +82 2 361 2868; fax: +82 2 312 4584; e-mail: ket@yeics,yon-sei.ac.kr. 1 This work was supported by the MIC (Ministry of Information and Communication) of Korea. 2 E-mail: mkpark@duck.snpu.ac.kr.

All Science Journal Classification (ASJC) codes

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


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