Title :
PWL nonlinear adaptive filter via RLS and NLMS algorithms
Author :
Plaziac, Nathalie ; Chon Tam Ledinh ; Adoul, Jean-Pierre
Author_Institution :
Dept. of Electr. Eng., Sherbrooke Univ., Que., Canada
fDate :
5/1/1997 12:00:00 AM
Abstract :
The recursive least square (RLS) and the normalized least mean square (NLMS) algorithms are proposed for canonical piecewise linear (PWL) adaptive filters. The parameters are updated recursively in a manner similar to back-propagation. The simulation results indicate PWL adaptive filters can suitably model nonlinear systems
Keywords :
adaptive filters; adaptive signal processing; filtering theory; least mean squares methods; least squares approximations; nonlinear filters; piecewise-linear techniques; recursive estimation; NLMS algorithm; PWL adaptive filters; RLS algorithm; back-propagation; canonical piecewise linear adaptive filters; nonlinear adaptive filter; normalized least mean square algorithm; recursive least square algorithm; simulation results; Adaptive filters; Least squares approximation; Least squares methods; Neural networks; Nonlinear filters; Piecewise linear approximation; Piecewise linear techniques; Resonance light scattering; Signal processing algorithms; Vectors;
Journal_Title :
Signal Processing, IEEE Transactions on
