Comparison of convergence behavior of distributed evolutionary digital filters

Author

Abe, Masahide ; Kawamata, Masayuki

Author_Institution

Graduate Sch. of Eng., Tohoku Univ., Sendai, Japan

Volume

2

fYear

2001

fDate

6-9 May 2001

Firstpage

729

Abstract

This paper proposes distributed evolutionary digital filters (EDFs). The EDF is an adaptive digital filter which is controlled by adaptive algorithm based on evolutionary computation. In the proposed method, a large population of the original EDF is divided into smaller subpopulations. Each sub-EDF has one subpopulation and executes the small-sized main loop of the original EDE. In addition, the distributed algorithm periodically selects promising individuals from each subpopulation. Then, they migrate to different subpopulations. Numerical examples show that the distributed EDF has a higher convergence rate and smaller steady-state value of the square error than the original one

Keywords

adaptive filters; convergence; digital filters; evolutionary computation; adaptive algorithm; adaptive digital filter; convergence behavior; convergence rate; distributed evolutionary digital filters; small-sized main loop; steady-state value; subpopulations; Adaptive algorithm; Adaptive control; Adaptive filters; Convergence; Digital filters; Distributed algorithms; Evolutionary computation; Filtering; Genetic algorithms; Programmable control;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 2001. ISCAS 2001. The 2001 IEEE International Symposium on

Conference_Location

Sydney, NSW

Print_ISBN

0-7803-6685-9

Type

conf

DOI

10.1109/ISCAS.2001.921174

Filename

921174