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

Research output: Chapter in Book/Report/Conference proceedingConference paper

6 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 languageEnglish
Title of host publication15th IFAC Symposium on System Identification (SYSID 2009), July 6-8, 2009, St. Malo, France, pp 816-819
Pages816-819
Number of pages4
Publication statusPublished - 6 Jul 2009
EventFinds and Results from the Swedish Cyprus Expedition: A Gender Perspective at the Medelhavsmuseet - Stockholm, Sweden
Duration: 21 Sep 200925 Sep 2009

Conference

ConferenceFinds and Results from the Swedish Cyprus Expedition: A Gender Perspective at the Medelhavsmuseet
CountrySweden
CityStockholm
Period21/09/0925/09/09

Keywords

  • identification
  • benchmark data

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