A novel adaptive kernel method with kernel centers determined by a support vector regression approach

Sun, L.G.; De Visser, C.C.; Chu, Q.P.; Mulder, J.A.

doi:doi:doi:10.1016/j.neucom.2013.07.023

A novel adaptive kernel method with kernel centers determined by a support vector regression approach

Title

A novel adaptive kernel method with kernel centers determined by a support vector regression approach

Author

Sun, L.G.
De Visser, C.C.
Chu, Q.P.
Mulder, J.A.

Faculty

Aerospace Engineering

Department

Control & Operations

Date

2012-11-05

Abstract

The optimality of the kernel number and kernel centers plays a significant role in determining the approximation power of nearly all kernel methods. However, the process of choosing optimal kernels is always formulated as a global optimization task, which is hard to accomplish. Recently, an algorithm, namely improved recursive reduced least squares support vector regression (IRR-LSSVR), was proposed for establishing a global nonparametric offline model, which demonstrates significant advantage in choosing representing and fewer support vectors compared with others. Inspired by the IRR- LSSVR, a new adaptive parametric kernel method called WV-LSSVR is proposed in this paper using the same type of kernels and the same centers as those used in the IRR-LSSVR. Furthermore, inspired by the multikernel semiparametric support vector regression, the effect of the kernel extension is investigated in a recursive regression framework, and a recursive kernel method called GPK-LSSVR is proposed using a compound type of kernels which are recommended for Gaussian process regression. Numerical experiments on benchmark data sets confirm the validity and effectiveness of the presented algorithms. The WV-LSSVR algorithm shows higher approximation accuracy than the recursive parametric kernel method using the centers calculated by the k-means clustering approach. The extended recursive kernel method (i.e. GPK-LSSVR) has not shown advantage in terms of global approximation accuracy when validating the test data set without real-time updation, but it can increase modeling accuracy if the real-time identification is involved.

Subject

support vector machine
recursive identification
adaptive model
kernel basis function

To reference this document use:

http://resolver.tudelft.nl/uuid:e070de9d-e805-4aa5-9bcc-7f8719bb56e1

Publisher

Elsevier

ISSN

0925-2312

Source

Neurocomputing, 124, 2014; Preprint

Other version

https://doi.org/doi:10.1016/j.neucom.2013.07.023