The fuzzy model identification problem from noisy data is addressed. The piecewise linear fuzzy model structure is used as a nonlinear prototype for a multi-input, single-output unknown system. The consequent of the fuzzy model is identified using noisy data, e.g. collected from experiments on a real system. The identification procedure is formulated within the Frisch scheme, well established for linear systems, which has been modified and improved to be applied in fuzzy systems field.
Noise rejection in parameters identification for piecewise linear fuzzy models / Simani, S.; Fantuzzi, C.; Rovatti, R.; Beghelli, S.. - 1:(1998), pp. 378-383. (Intervento presentato al convegno 1998 IEEE International Conference on Fuzzy Systems, FUZZY 1998 tenutosi a usa nel 1998) [10.1109/FUZZY.1998.687515].
Noise rejection in parameters identification for piecewise linear fuzzy models
Simani S.;Fantuzzi C.;Beghelli S.
1998
Abstract
The fuzzy model identification problem from noisy data is addressed. The piecewise linear fuzzy model structure is used as a nonlinear prototype for a multi-input, single-output unknown system. The consequent of the fuzzy model is identified using noisy data, e.g. collected from experiments on a real system. The identification procedure is formulated within the Frisch scheme, well established for linear systems, which has been modified and improved to be applied in fuzzy systems field.
Pubblicazioni consigliate
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris
