Difference between revisions of "Bp 175 en"

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of fuzzy c-means (FCM) and Gustafson-Kessel (GK) algorithms in this project. The
 
of fuzzy c-means (FCM) and Gustafson-Kessel (GK) algorithms in this project. The
 
verification of approximation on two or three dimensional data ensues next. The time
 
verification of approximation on two or three dimensional data ensues next. The time
and the quality of approximation are measured by verification of results
+
and the quality of approximation are measured by verification of results.
 +
 
 +
* '''Mik Petr''', mailto:mikp2@fel.cvut.cz
 +
* '''Hušek Petr''', mailto:husek@fel.cvut.cz

Revision as of 22:50, 4 May 2010

Function Approximation Using Fuzzy Clustering

Author: Petr Mik

Bakalářské práce 2008

This bachelor’s project is focused on the approximation of function with use algorithms and methods of fuzzy clustering analysis for recognition of data, and Mamdani or Takagi-Sugeno fuzzy systems for approximation of desired function. There are mentioned the basic concept of adjustment parameters of this systems with the aid of fuzzy c-means (FCM) and Gustafson-Kessel (GK) algorithms in this project. The verification of approximation on two or three dimensional data ensues next. The time and the quality of approximation are measured by verification of results.