Title :
Evolutionary variable step-size algorithm for adaptive filtering
Author :
Chung, C.Y. ; Leung, S.H. ; Ng, S.C. ; Luk, A.
Author_Institution :
Dept. of Electron. Eng., City Univ. of Hong Kong, Kowloon, Hong Kong
fDate :
29 Nov-1 Dec 1995
Abstract :
A new variable step size scheme namely evolutionary variable step-size algorithm (EVS) for adaptive filtering is proposed. The algorithm is basically a kind of evolutionary method based on evolving the step size of the least-mean-square (LMS) algorithm. The step size candidates are generated by random or deterministic perturbation and then evaluated by calculating a square error measure based on a priori and a posteriori errors. The fittest candidate is selected for subsequent adaptation. The composition of the square error measure is regulated according to the mode of adaptation in order to provide fast converging and tracking capability. The convergence performance is significantly improved and is less sensitive to the eigenvalue spread
Keywords :
adaptive filters; convergence of numerical methods; eigenvalues and eigenfunctions; filtering theory; genetic algorithms; least mean squares methods; perturbation techniques; random processes; a posteriori errors; a priori errors; adaptation mode; adaptive filtering; convergence performance; deterministic perturbation; eigenvalue spread; evolutionary variable step-size algorithm; fittest candidate; least-mean-square algorithm; random perturbation; square error measure; step size candidates; tracking; Adaptive filters; Convergence; Eigenvalues and eigenfunctions; Equations; Evolutionary computation; Filtering algorithms; Genetic algorithms; Least squares approximation; Size measurement; Steady-state;
Conference_Titel :
Evolutionary Computation, 1995., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2759-4
DOI :
10.1109/ICEC.1995.487463
