Title :
An improved stochastic model of the NLMS algorithm for correlated input data
Author :
Kolodziej, Javier E. ; Tobias, Orlando J. ; Seara, Rui
Author_Institution :
Dept. of Electr. Eng., Fed. Univ. of Santa Catarina, Florianópolis, Brazil
Abstract :
This paper proposes an improved stochastic model for the normalized least-mean-square (NLMS) algorithm considering correlated input signals obtained from a spherically invariant random process (SIRP). A SIRP describes both Gaussian and a wide class of non-Gaussian processes, including the ones with Laplacian, K0, and Gamma marginal density functions. Hence an approximate procedure for computing high-order hyperelliptic integrals arisen from the modeling process is developed. The resulting model outperforms other existing models discussed in the open literature. Through numerical simulations the accuracy of the proposed model is verified.
Keywords :
Gaussian processes; approximation theory; correlation theory; elliptic equations; least mean squares methods; random processes; Gamma marginal density function; Gaussian process; K0 function; Laplacian function; NLMS algorithm; SIRP; approximate procedure; correlated input signal; hyperelliptic integrals; improved stochastic model; nonGaussian process; normalized least mean square algorithm; numerical simulation; spherically invariant random process; Adaptation models; Adaptive filters; Computational modeling; Data models; Mathematical model; Signal processing algorithms;
Conference_Titel :
Signal Processing Conference, 2007 15th European
Conference_Location :
Poznan
Print_ISBN :
978-839-2134-04-6