DocumentCode
2348754
Title
Design and comparative study of the RKHS model reduction techniques
Author
Okba, Taouali ; Ilyes, Elaissi ; Hassani, Messaouad
Author_Institution
Res. Unit ATSI, Nat. Eng. Sch. of Monastir, Monastir, Tunisia
fYear
2010
fDate
3-5 March 2010
Firstpage
1
Lastpage
5
Abstract
The paper proposes the design and comparative study of two reduction methods of these models. The first, titled support vector regression (SVR) and the second is the projection method. Both methods use the Statistical Learning Theory (SLT) which operates on Reproducing Kernel Hilbert Space (RKHS) space. The performances of both methods are evaluated on the Tennessee Eastman process.
Keywords
Hilbert spaces; computational complexity; regression analysis; support vector machines; RKHS; RKHS model reduction techniques; SLT; SVR; Tennessee Eastman process; projection method; reproducing Kernel Hilbert space; statistical learning theory; support vector regression; Biomedical signal processing; Communication system control; Hilbert space; Kernel; Nonlinear control systems; Nonlinear systems; Process control; Reduced order systems; Signal design; Statistical learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Communications, Control and Signal Processing (ISCCSP), 2010 4th International Symposium on
Conference_Location
Limassol
Print_ISBN
978-1-4244-6285-8
Type
conf
DOI
10.1109/ISCCSP.2010.5463422
Filename
5463422
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