Identification of Wiener-Hammerstein Benchmark Data by Means of Support Vector Machines

Anna Marconato; Joannes Schoukens

Identification of Wiener-Hammerstein Benchmark Data by Means of Support Vector Machines

Anna Marconato, Joannes Schoukens

Elektriciteit

Onderzoeksoutput: Conference paper

9 Citaten (Scopus)

Samenvatting

This work presents the identification of a Wiener-Hammerstein system by a learning-fromexamples approach, namely the Support Vector Machines for Regression, on the basis of a set of real-life benchmark data. A multi-objective optimization procedure based on genetic algorithms is employed in order to select the best model that describes the input-output relationship of the considered system. Training sets of reduced size are employed to analyze the effect on the accuracy performance.

Originele taal-2	English
Titel	15th IFAC Symposium on System Identification (SYSID 2009), July 6-8, 2009, St. Malo, France, pp 816-819
Pagina's	816-819
Aantal pagina's	4
Status	Published - 6 jul. 2009
Evenement	Unknown - Stockholm, Sweden Duur: 21 sep. 2009 → 25 sep. 2009

Conference

Conference	Unknown
Land/Regio	Sweden
Stad	Stockholm
Periode	21/09/09 → 25/09/09

Citeer dit

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abstract = "This work presents the identification of a Wiener-Hammerstein system by a learning-fromexamples approach, namely the Support Vector Machines for Regression, on the basis of a set of real-life benchmark data. A multi-objective optimization procedure based on genetic algorithms is employed in order to select the best model that describes the input-output relationship of the considered system. Training sets of reduced size are employed to analyze the effect on the accuracy performance.",

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AU - Schoukens, Joannes

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AB - This work presents the identification of a Wiener-Hammerstein system by a learning-fromexamples approach, namely the Support Vector Machines for Regression, on the basis of a set of real-life benchmark data. A multi-objective optimization procedure based on genetic algorithms is employed in order to select the best model that describes the input-output relationship of the considered system. Training sets of reduced size are employed to analyze the effect on the accuracy performance.

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KW - benchmark data

M3 - Conference paper

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BT - 15th IFAC Symposium on System Identification (SYSID 2009), July 6-8, 2009, St. Malo, France, pp 816-819

T2 - Finds and Results from the Swedish Cyprus Expedition: A Gender Perspective at the Medelhavsmuseet

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Identification of Wiener-Hammerstein Benchmark Data by Means of Support Vector Machines

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