ISSN : 1796-2021
Volume : 2    Issue : 3    Date : May 2007

Nonlinear System Identification using a New Sliding-Window Kernel RLS Algorithm
Steven Van Vaerenbergh, Javier Vía, and Ignacio Santamaría
Page(s): 1-8
Full Text:
PDF (656 KB)

In this paper we discuss in detail a recently proposed kernel-based version of the recursive
least-squares (RLS) algorithm for fast adaptive nonlinear filtering. Unlike other previous
approaches, the studied method combines a sliding-window approach (to fix the dimensions of the
kernel matrix) with conventional ridge regression (to improve generalization). The resulting kernel
RLS algorithm is applied to several nonlinear system identification problems. Experiments show
that the proposed algorithm is able to operate in a time-varying environment and to adjust to abrupt
changes in either the linear filter or the nonlinearity.

Index Terms
kernel methods, kernel RLS, sliding-window, system identification