Title :
Relative convergence of the cascade RLS with subsection adaptation algorithm
Author :
Zakaria, G. ; Beex, A.A.
Author_Institution :
Hughes Network Syst. Inc., Germantown, MD, USA
Abstract :
We analyze the convergence behavior of the CRLS-SA algorithm for inverse filtering. The CRLS-SA is a cascade adaptive filter based on the RLS algorithm, with each section adapted independently based on global minimization. The subsection adaptation results in reduced computational complexity. The rate of convergence is evaluated based on the convergence time constant defined as the ratio of condition number and sensitivity. The smaller the convergence time constant, the faster the structure converges. Analysis and simulation explain and show that CRLS-SA exhibits faster convergence than the direct form RLS adaptive filter for speech type signals.
Keywords :
adaptive filters; adaptive signal processing; cascade networks; circuit optimisation; computational complexity; convergence of numerical methods; filtering theory; inverse problems; least squares approximations; minimisation; recursive filters; speech processing; CRLS-SA algorithm; RLS algorithm; cascade RLS; cascade adaptive filter; condition number to sensitivity ratio; convergence behavior; convergence rate; convergence time constant; direct form RLS adaptive filter; global minimization; inverse filtering; reduced computational complexity; relative convergence; simulation; speech type signals; subsection adaptation algorithm; Adaptive filters; Algorithm design and analysis; Computational complexity; Computational modeling; Convergence; Filtering algorithms; Minimization methods; Resonance light scattering; Signal analysis; Speech analysis;
Conference_Titel :
Signals, Systems, and Computers, 1999. Conference Record of the Thirty-Third Asilomar Conference on
Print_ISBN :
0-7803-5700-0
DOI :
10.1109/ACSSC.1999.832441
