A cluster extension method with extension to fuzzy model identification

Author

Chiu, Stephen L.

Author_Institution

Rockwell Int. Sci. Center, Thousand Oaks, CA, USA

fYear

1994

fDate

26-29 Jun 1994

Firstpage

1240

Abstract

We present an efficient method for estimating cluster centers of numerical data. This method can be used to determine the number of clusters and their initial values for initializing iterative optimization-based clustering algorithms such as fuzzy C-means. Here were combine this cluster estimation method with a least squares estimation algorithm to provide a fast and robust method for identifying fuzzy models from input/output data. A benchmark problem involving the prediction of a chaotic time series shows this method compares favourably with other more compositionally intensive methods

Keywords

chaos; fuzzy set theory; identification; iterative methods; least mean squares methods; optimisation; time series; chaotic time series; cluster estimation; cluster extension method; fuzzy C-means; fuzzy model identification; iterative optimization; least squares estimation; numerical data; Chaos; Clustering algorithms; Cost function; Grid computing; Iterative algorithms; Iterative methods; Least squares approximation; Modeling; Optimization methods; Robustness;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the Third IEEE Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1896-X

Type

conf

DOI

10.1109/FUZZY.1994.343644

Filename

343644