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

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

doi:10.1007/978-3-540-30497-5_84

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

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

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article

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 language	English
Pages (from-to)	538-543
Number of pages	6
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3314
DOIs	https://doi.org/10.1007/978-3-540-30497-5_84
Publication status	Published - 2004

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

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10.1007/978-3-540-30497-5_84

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Kim, K., Kyung, K. M., Park, C. W., Kim, E., & Park, M. (2004). Robust TSK Fuzzy Modeling Approach Using Noise Clustering Concept for Function Approximation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3314, 538-543. https://doi.org/10.1007/978-3-540-30497-5_84

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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, 2004, p. 538-543.

Research output: Contribution to journal › Article

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