Title :
Statistical analysis of the LMS algorithm with a zero-memory nonlinearity after the adaptive filter
Author :
Costa, Márcio H. ; Bermudez, José C M ; Bershad, Neil J.
Author_Institution :
Biomed. Instrum. Group, Univ. Catolica de Pelotas, Pelotas, Brazil
Abstract :
This paper presents a statistical analysis of the least mean square (LMS) algorithm when a zero-memory nonlinearity appears at the adaptive filter output. The nonlinearity is modelled by a scaled error function. Deterministic nonlinear recursions are derived for the mean weight and mean square error (MSE) behavior for white Gaussian inputs and slow adaptation. Monte Carlo simulations show excellent agreement with the behavior predicted by the theoretical models. The analytical results show that a small nonlinear effect has a significant impact on the converged MSE
Keywords :
Monte Carlo methods; adaptive filters; adaptive signal processing; digital simulation; filtering theory; least mean squares methods; nonlinear filters; statistical analysis; LMS algorithm; Monte Carlo simulations; adaptive filter; converged MSE; deterministic nonlinear recursions; least mean square; mean square error; mean weight; nonlinear effect; scaled error function; slow adaptation; statistical analysis; white Gaussian inputs; zero-memory nonlinearity; Adaptive filters; Biomedical computing; Biomedical engineering; Biomedical measurements; Computer errors; Electronic mail; Equations; Least squares approximation; Predictive models; Statistical analysis;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1999. Proceedings., 1999 IEEE International Conference on
Conference_Location :
Phoenix, AZ
Print_ISBN :
0-7803-5041-3
DOI :
10.1109/ICASSP.1999.756311
