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IJSYSC
2000

Incorporating prior knowledge in fuzzy model identification

13 years 3 months ago
Incorporating prior knowledge in fuzzy model identification
This paper presents an algorithm for incorporating a priori knowledge into data-driven identification of dynamic fuzzy models of the Takagi-Sugeno type. Knowledge about the modelled process such as its stability, minimal or maximal static gain, or the settling time of its step response can be translated into inequality constraints on the consequent parameters. By using input-output data, optimal parameter values are then found by means of quadratic programming. The proposed approach has been applied to the identification of a laboratory liquid level process. The obtained fuzzy model has been used in model-based predictive control. Real-time control results show that when the proposed identification algorithm is applied, not only physically justified models are obtained, but also the performance of the model-based controller improves with regard to the case where no prior knowledge is involved.
János Abonyi, Robert Babuska, Henk B. Verbr
Added 18 Dec 2010
Updated 18 Dec 2010
Type Journal
Year 2000
Where IJSYSC
Authors János Abonyi, Robert Babuska, Henk B. Verbruggen, Ferenc Szeifert
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