Title :
Simplification of stochastic fastest NLMS algorithm
Author :
Fukumoto, Masahiro ; Kubota, Hajime ; Tsuji, Satoshi
Author_Institution :
Kochi Univ. of Technol., Japan
Abstract :
The behavior of the normalized LMS (NLMS) algorithm is unstable when all elements of an input state vector are very small. This property is caused by the division in the procedure of the NLMS algorithm. It has been taken as one of the countermeasures against the above problem that coefficients of an adaptive filter are not updated when a norm of an input state vector is smaller than a threshold. The guarantee value (least upper bound of the estimation error) and the stochastic fastest convergence step gain with interruption of coefficients update have been shown. In this paper, we show the simplification of the above method. As a result, the proposed algorithm has no need for statistics which are difficult to obtain under normal conditions
Keywords :
adaptive filters; convergence of numerical methods; filtering theory; least mean squares methods; stochastic processes; adaptive filter; coefficients update; estimation error; input state vector norm; least upper bound; normalized LMS algorithm; stochastic fastest NLMS algorithm; stochastic fastest convergence step gain; Adaptive filters; Convergence; Error correction; Estimation error; Least squares approximation; Signal to noise ratio; Statistics; Stochastic processes; Stochastic resonance; Upper bound;
Conference_Titel :
Circuits and Systems, 1999. ISCAS '99. Proceedings of the 1999 IEEE International Symposium on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-5471-0
DOI :
10.1109/ISCAS.1999.778809
