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Fuzzy Clustering for System Identification

Abstract

In this chapter we deal with fuzzy model identification, especially by dynamical systems. In practice, there is a need for model-based engineering tools, and they require the availability of suitable dynamical models. Consequently, the development of a suitable nonlinear model is of paramount importance. Fuzzy systems have been effectively used to identify complex nonlinear dynamical systems. In this chapter we would like to show how effectively clustering algorithms can be used to identify a compact Takagi-Sugeno fuzzy model to represent single-input single-output and also multiple-input multiple-output dynamical systems.

Keywords

Fuzzy Model Fuzzy Cluster Minimum Description Length Chaotic Time Series Linear Parameter Vary 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Birkhäuser Verlag AG 2007

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