A method for fuzzy system identification based on clustering analysis

  • Authors:

  • George Tsekouras;Haralambos Sarimveis;George Bafas

  • Affiliations:

  • National Technical University of Athens, Department of Chemical Engineering, 9, Heroon Polytechniou str., Zografou Campus, Athens 15780, Greece;National Technical University of Athens, Department of Chemical Engineering, 9, Heroon Polytechniou str., Zografou Campus, Athens 15780, Greece;National Technical University of Athens, Department of Chemical Engineering, 9, Heroon Polytechniou str., Zografou Campus, Athens 15780, Greece

  • Venue:
  • Systems Analysis Modelling Simulation
  • Year:
  • 2002

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Abstract

In this paper a methodology which identifies fuzzy systems is developed. In the first place, an algorithm that employs a distance function to automatically generate fuzzy rules is proposed. In addition to that, this algorithm gives an estimation of the system parameters, which are used as initial values for the iterative parameter optimization that follows. A clustering analysis is adopted to optimize the premise parameters and the least-squares method is used to optimize the consequent parameters. The number of rules is controlled by the performance of the system. Finally, simulations show the validity of the proposed method.