@inproceedings{c83052111c2d409fa1038d83925f84c4,
title = "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.",
keywords = "Fuzzy inverse control, Genetic algorithm, Internal model control",
author = "Miguel Ramirez and Eliezer Colina",
year = "2010",
language = "English (US)",
isbn = "9789604742578",
series = "International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics - Proceedings",
pages = "177--182",
booktitle = "Advances in Computational Intelligence, Man-Machine Systems and Cybernetics - 9th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics, CIMMACS'10",
note = "9th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics, CIMMACS'10 ; Conference date: 14-12-2010 Through 16-12-2010",
}