Title :
A cluster extension method with extension to fuzzy model identification
Author :
Chiu, Stephen L.
Author_Institution :
Rockwell Int. Sci. Center, Thousand Oaks, CA, USA
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;
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
DOI :
10.1109/FUZZY.1994.343644
