DocumentCode :
104526
Title :
Hybrid kernel identification method based on support vector regression and regularisation network algorithms
Author :
Taouali, Okba ; Elaissi, Ilyes ; Messaoud, Hassani
Author_Institution :
Nat. Sch. of Eng. of Monastir, Univ. of Monastir, Tunisia
Volume :
8
Issue :
9
fYear :
2014
fDate :
12 2014
Firstpage :
981
Lastpage :
989
Abstract :
This study proposes a new kernel method for online identification of a non-linear system modelled on reproducing kernel Hilbert space (RKHS). The proposed method is a concatenation of two techniques proposed in the literature, the support vector regression and the Regularisation Networks (RNs). The proposed algorithm, called the online SVR-RN kernel method, uses first the SVR in an offline phase to construct an RKHS model with a reduced parameter number and second the RN method in an online phase to update the model parameters. The proposed algorithm has been tested to identify the chemical Tennessee Eastman Process and the electronic non-linear system with a Wiener Hammerstein structure.
Keywords :
Hilbert spaces; chemical engineering; nonlinear control systems; regression analysis; support vector machines; RKHS model; Wiener Hammerstein structure; chemical Tennessee Eastman Process; electronic nonlinear system; hybrid kernel identification method; nonlinear system; online SVR-RN kernel method; online identification; regularisation network algorithms; reproducing kernel Hilbert space; support vector regression;
fLanguage :
English
Journal_Title :
Signal Processing, IET
Publisher :
iet
ISSN :
1751-9675
Type :
jour
DOI :
10.1049/iet-spr.2013.0242
Filename :
6994383
Link To Document :
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