<title>Optimal RASF filtering</title>

Abstract

The recursive approaching signal filter (RASF) calculates the weights for each filtering window position from the difference of the original signal and a prefiltered signal. The original definition suggests the use of an exponential function for calculating the weights, but any nonincreasing function may be used as well. This paper addresses the problem of selecting the optimal one among them via empirical simulations applying the programming paradigm of genetic algorithms for the optimization problem. Furthermore, another modification to the RASF filter class taking advantage of a larger number of observations with smaller time complexity is proposed and thus a novel filter class is presented. The designed optimization scheme for finding the optimal weighting function is applied also to these filters and comparisons with the RASF filter are presented.