Modifications to the sliding-window kernel RLS algorithm for time-varying nonlinear systems: Online resizing of the kernel matrix
Author(s)Julian, Brian John
DownloadJulian-2009-MODIFICATIONS TO THE SLIDING-WINDOW KERNEL RLS ALGORITHM FOR TIME-VARYING NONLINEAR SYSTEMS ONLINE RESIZING .pdf (335.9Kb)
MetadataShow full item record
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.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
IEEE International Conference on Acoustics, Speech and Signal Processing, 2009. ICASSP 2009.
Institute of Electrical and Electronics Engineers
INSPEC Accession Number: 10701149
time-varying filters, nonlinear filters, least squares methods, learning systems, identification