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

Electricity

Research output: Chapter in Book/Report/Conference proceeding › Conference paper

9 Citations (Scopus)

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.

Original language	English
Title of host publication	15th IFAC Symposium on System Identification (SYSID 2009), July 6-8, 2009, St. Malo, France, pp 816-819
Pages	816-819
Number of pages	4
Publication status	Published - 6 Jul 2009
Event	Finds and Results from the Swedish Cyprus Expedition: A Gender Perspective at the Medelhavsmuseet - Stockholm, Sweden Duration: 21 Sept 2009 → 25 Sept 2009

Conference

Conference	Finds and Results from the Swedish Cyprus Expedition: A Gender Perspective at the Medelhavsmuseet
Country/Territory	Sweden
City	Stockholm
Period	21/09/09 → 25/09/09

Keywords

identification
benchmark data

Handle.net

Persistent link

Cite this

@inproceedings{a485048cef5344cca9ce7cd4526d44ef,

title = "Identification of Wiener-Hammerstein Benchmark Data by Means of Support Vector Machines",

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.",

keywords = "identification, benchmark data",

author = "Anna Marconato and Joannes Schoukens",

year = "2009",

month = jul,

day = "6",

language = "English",

isbn = "978-3-902661-47-0",

pages = "816--819",

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

note = "Finds and Results from the Swedish Cyprus Expedition: A Gender Perspective at the Medelhavsmuseet ; Conference date: 21-09-2009 Through 25-09-2009",

}

Marconato, A & Schoukens, J 2009, Identification of Wiener-Hammerstein Benchmark Data by Means of Support Vector Machines. in 15th IFAC Symposium on System Identification (SYSID 2009), July 6-8, 2009, St. Malo, France, pp 816-819. pp. 816-819, Finds and Results from the Swedish Cyprus Expedition: A Gender Perspective at the Medelhavsmuseet, Stockholm, Sweden, 21/09/09.

TY - GEN

T1 - Identification of Wiener-Hammerstein Benchmark Data by Means of Support Vector Machines

AU - Marconato, Anna

AU - Schoukens, Joannes

PY - 2009/7/6

Y1 - 2009/7/6

N2 - 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.

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.

KW - identification

KW - benchmark data

M3 - Conference paper

SN - 978-3-902661-47-0

SP - 816

EP - 819

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

Y2 - 21 September 2009 through 25 September 2009

ER -

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

Abstract

Conference

Keywords

Handle.net

Fingerprint

Cite this