Nonlinear System Identification using a New Sliding-Window Kernel RLS Algorithm

TitleNonlinear System Identification using a New Sliding-Window Kernel RLS Algorithm
Publication TypeJournal Article
Year of Publication2007
AuthorsS. Van Vaerenbergh, J. Vía, and I. Santamaría
JournalJournal of Communications
Volume2
Pagination1–8
Month PublishedMay
Keywordskernel adaptive filtering, KRLS
AbstractIn 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.