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Hybrid kernel identification method based on support vector regression and regularisation network algorithms

Content Provider	IET Digital Library
Author	Taouali, Okba Elaissi, Ilyes Messaoud, Hassani
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.
Starting Page	981
Ending Page	989
Page Count	9
ISSN	17519675
Volume Number	8
e-ISSN	17519683
Issue Number	Issue 9, Dec (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/8/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2013.0242
Journal	IET Signal Processing
Publisher Date	2014-12-15
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Chemical Engineering Chemical Engineering Computing Chemical Industry Chemical Tennessee Eastman Process Electronic Nonlinear System Hilbert Space Hybrid Kernel Identification Method Industrial Processes Nonlinear Control System Nonlinear System Online Identification Online SVR-RN Kernel Method Regression Analysis Regularisation Network Algorithm Reproducing Kernel Hilbert Space RKHS Model Statistics Support Vector Machine Support Vector Regression Wiener Hammerstein Structure
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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