In this paper we exploit the approximation capabilities of Takagi-Sugeno models to devise an identification procedure for Single-Input Single-Output systems, which minimizes the squared error between the model and the target. The adoption of a model featuring an increased locality allows a substantial reduction in the complexity of the identification phase in which samples are taken into account. Then, a data-independent mapping is devised to translate modified Takagi-Sugeno models into conventional ones.
Efficient Least Squares Identification with SISO Takagi--Sugeno Models / Fantuzzi, Cesare; R., Rovatti. - STAMPA. - (1997), pp. 585-589. (Intervento presentato al convegno Intelligent Components and Instruments for Control Applications 1997 tenutosi a Annecy, France nel 9-11 June 1997).
Efficient Least Squares Identification with SISO Takagi--Sugeno Models
FANTUZZI, Cesare;
1997
Abstract
In this paper we exploit the approximation capabilities of Takagi-Sugeno models to devise an identification procedure for Single-Input Single-Output systems, which minimizes the squared error between the model and the target. The adoption of a model featuring an increased locality allows a substantial reduction in the complexity of the identification phase in which samples are taken into account. Then, a data-independent mapping is devised to translate modified Takagi-Sugeno models into conventional ones.Pubblicazioni consigliate
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