Modifications to the sliding-window kernel RLS algorithm for time-varying nonlinear systems: Online resizing of the kernel matrix

A kernel-based recursive least-squares algorithm that implements a fixed size ldquosliding-windowrdquo technique has been recently proposed for fast adaptive nonlinear filtering applications. We propose a methodology of resizing the kernel matrix to assist in system identification of time-varying no...

Full description

Saved in:
Bibliographic Details
Published in2009 IEEE International Conference on Acoustics, Speech and Signal Processing pp. 3389 - 3392
Main Author Julian, B.J.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2009
Subjects
Adaptive filters
Filtering algorithms
identification
Kernel
learning systems
least squares methods
Machine learning algorithms
Nonlinear filters
Nonlinear systems
Resonance light scattering
Signal processing algorithms
System identification
Time varying systems
time-varying filters
Online AccessGet full text

Cover

More Information
Summary:A kernel-based recursive least-squares algorithm that implements a fixed size ldquosliding-windowrdquo technique has been recently proposed for fast adaptive nonlinear filtering applications. We propose a methodology of resizing the kernel matrix to assist in system identification of time-varying nonlinear systems. To be applicable in practice, the modified algorithm must preserve its ability to operate online. Given a bound on the maximum kernel matrix size, we define the set of all obtainable sizes as the resizing range. We then propose a simple online technique that resizes the kernel matrix within the resizing range. The modified algorithm is applied to the nonlinear system identification problem that was used to evaluate the original algorithm. Results show that an increase in performance is achieved without increasing the original algorithm's computation time.
ISBN:9781424423538
1424423538
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2009.4960352