A genetic-algorithm based approach for generating fuzzy singleton models

Abstract:

Methods for generating fuzzy singleton models from input-output data have been proposed by many authors. This paper introduces a genetic algorithm (GA) based method to generate a fuzzy singleton model taking into account all the necessary constraints to guarantee an analytically inverted representation of the process dynamics which may be used as a fuzzy controller in Internal Model Control (IMC) strategy. A major advantage of this sort of models is its high interpretability compared to first-order Takagi-Sugeno fuzzy models generated from fuzzy clustering techniques [15]. The proposed method is applied to a liquid level control problem in an oil production separator based upon real input-output data, where obtaining an adequate fuzzy model is of crucial importance.

Año de publicación:

2010

Keywords:

• Genetic Algorithm
• Internal model control
• Fuzzy inverse control

Fuente:

scopus