Wind prediction using complex augmented adaptive filters

Abstract

In this paper we discuss a new set of nonlinear adaptive filters based on kernel methods and compare them to the least mean square (LMS) and recursive least squares (RLS) adaptive filters. In recent years a new class of nonlinear kernel adaptive filters have been developed that tradeoff performance for complexity including the Kernel LMS (KLMS) and Kernel RLS (KRLS) algorithms. Earlier work discussed a complex augmented implementation of the kernel algorithms. This paper continues this discussion and compares the performance and complexity of the algorithms for wind time series prediction.