DocumentCode
706522
Title
Support vector regression and NARMAX system identification
Author
Drezet, P. ; Harrison, R.F.
Author_Institution
Dept. of Autom. Control & Syst. Eng., Univ. of Sheffield, Sheffield, UK
fYear
1999
fDate
Aug. 31 1999-Sept. 3 1999
Firstpage
1161
Lastpage
1165
Abstract
Support Vector Regression (SVR) is a flexible regression method, which can be applied directly to NARMAX system identification models. SVR is a one-step convex optimisation process which attempts to maximise generalisation performance. This paper compares SVR performance with that of multi-layer perceptrons and radial basis function networks for varying numbers of time lags included in the model.
Keywords
convex programming; identification; nonlinear control systems; regression analysis; support vector machines; NARMAX system identification; SVR; convex optimisation process; support vector regression; Computational modeling; Data models; Mathematical model; Neural networks; Optimization; Support vector machines; Training; NARMAX; Nonlinear Regression; Support Vector Regression; System Identification;
fLanguage
English
Publisher
ieee
Conference_Titel
Control Conference (ECC), 1999 European
Conference_Location
Karlsruhe
Print_ISBN
978-3-9524173-5-5
Type
conf
Filename
7099466
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