Title :
A generalized normalized gradient descent algorithm
Author :
Mandic, Danilo P.
Author_Institution :
Dept. of Electr. & Electron. Eng., Imperial Coll., London, UK
Abstract :
A generalized normalized gradient descent (GNGD) algorithm for linear finite-impulse response (FIR) adaptive filters is introduced. The GNGD represents an extension of the normalized least mean square (NLMS) algorithm by means of an additional gradient adaptive term in the denominator of the learning rate of NLMS. This way, GNGD adapts its learning rate according to the dynamics of the input signal, with the additional adaptive term compensating for the simplifications in the derivation of NLMS. The performance of GNGD is bounded from below by the performance of the NLMS, whereas it converges in environments where NLMS diverges. The GNGD is shown to be robust to significant variations of initial values of its parameters. Simulations in the prediction setting support the analysis.
Keywords :
FIR filters; adaptive filters; gradient methods; least mean squares methods; prediction theory; generalized normalized gradient descent algorithm; gradient adaptive term learning rate; input signal; linear finite-impulse response adaptive filter; nonlinear prediction; normalized least mean square algorithm; Adaptive filters; Analytical models; Computational complexity; Convergence; Filtering; Finite impulse response filter; Least squares approximation; Predictive models; Robustness; Signal processing algorithms;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2003.821649
