fuzzy clustering analysis and nonlinear systems
I graduated bachelors studies on Faculty of Electrical Engineering Faculty at CTU with bachelor thesis about system identification methods with fuzzy clustering analysis in 2008.
goals
- introduce yourself to basic methods of fuzzy clustering - fuzzy c-means algorithms, Gustafson-Kessel algorithm
- examine usability these algorithms for modeling nonlinear systems set by bachelor thesis supervisor with Takagi-Sugeno fuzzy models
abstract
This paper explores fuzzy clustering methods and their usage for modeling nonlinear systems. We discuss methods fuzzy c-means and Gustafson-Kessel algorithm. Further we focused Takagi-Sugeno models, their principle and assembling using fuzzy clustering methods quoted above. Takagi-Sugeno models were tested on static nonlinear system where we verified approximation capability and on dynamic discrete nonlinear system, where algorithms were searching for fuzzy clusters in four dimensions.
fuzzy clustering methods for modeling nonlinear systems
You can
download full article in pdf (and in czech).
last modified 15.12.2010
comments