Adaptive weighted order statistic filters using back propagation algorithm
Le Yin, Jaakko T. Astola, Yrjo A. Neuvo · 2002
An adaptive weighted order statistic (WOS) filter is proposed. It can adaptively estimate the parameters of WOS filters according to its inputs and outputs. Since the number of variables of a WOS filter is equal to its window width, this adaptive algorithm is quite efficient. Another distinct advantage is that the adaptive WOS filter can proceed without use of threshold decomposition, which means that any discrete-time continuous value can be used as the input of the WOS filter. Some deterministic properties of WOS filters are discussed. A neural network structure is designed to realize this special stack filter. A learning algorithm is proposed to obtain the parameters of WOS filters. Some simulation results are presented to demonstrate the performance of the learning algorithm.>