Online data centering modifications for adaptive filtering with NLMS algorithm

This paper presents method with two modifications how to transform data in real-time for better performance of normalized least mean squares (NLMS) algorithm. The method centers input vector for adaptive filter online according to temporary or historical statistical attributes of the input vector. The method is derived for an adaptive filter with NLMS adaptation. The filter implementation is the linear neural unit. The stability condition for the given filter is also presented. The filter is tested on multiple simulated time series contaminated with white noise. The convergence of the suggested algorithms is also analyzed and time complexity is discussed.