Robust TSK Fuzzy Modeling Approach Using Noise Clustering Concept for Function Approximation

Kyoungjung Kim, Kyu Min Kyung, Chang Woo Park, Euntai Kim, Mignon Park

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

This paper proposes the algorithm that additional term is added to an objective function of noise clustering algorithm to define fuzzy subspaces in a fuzzy regression manner to identify fuzzy subspaces and parameters of the consequent parts simultaneously and obtain robust performance against outliers.

Original languageEnglish
Pages (from-to)538-543
Number of pages6
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3314
Publication statusPublished - 2004 Dec 1

Fingerprint

Fuzzy Subspace
Fuzzy Modeling
Function Approximation
Clustering algorithms
Clustering
Fuzzy Regression
Fuzzy Parameters
Robust Performance
Outlier
Clustering Algorithm
Objective function
Term
Concepts

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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Robust TSK Fuzzy Modeling Approach Using Noise Clustering Concept for Function Approximation. / Kim, Kyoungjung; Kyung, Kyu Min; Park, Chang Woo; Kim, Euntai; Park, Mignon.

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 3314, 01.12.2004, p. 538-543.

Research output: Contribution to journalArticle

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