Modifications to the sliding-window kernel RLS algorithm for time-varying nonlinear systems: Online resizing of the kernel matrix
Name
Julian-2009-MODIFICATIONS TO THE SLIDING-WINDOW KERNEL RLS ALGORITHM FOR TIME-VARYING NONLINEAR SYSTEMS ONLINE RESIZING .pdf
Size
335.94 KB
Format
Adobe PDF
Checksum (MD5)
b2d2eac01dc37426d0fd2b1efa0801cd
Author(s)
Julian, Brian John
Date Issued
May 2009
Journal
IEEE International Conference on Acoustics, Speech and Signal Processing, 2009. ICASSP 2009.
Publisher
Institute of Electrical and Electronics Engineers
Version
Final published version
Abstract
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.
Subjects
time-varying filters
nonlinear filters
least squares methods
learning systems
identification
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICASSP.2009.4960352
