Comparison of convergence behavior of distributed evolutionary digital filters

Masahide Abe; Masayuki Kawamata

doi:10.1109/ISCAS.2001.921174

Comparison of convergence behavior of distributed evolutionary digital filters

Masahide Abe, Masayuki Kawamata

ENG - Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

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.

Original language	English
Title of host publication	ISCAS 2001 - 2001 IEEE International Symposium on Circuits and Systems, Conference Proceedings
Pages	729-732
Number of pages	4
DOIs	https://doi.org/10.1109/ISCAS.2001.921174
Publication status	Published - 2001
Event	2001 IEEE International Symposium on Circuits and Systems, ISCAS 2001 - Sydney, NSW, Australia Duration: 2001 May 6 → 2001 May 9

Publication series

Name	ISCAS 2001 - 2001 IEEE International Symposium on Circuits and Systems, Conference Proceedings
Volume	2

Other

Other	2001 IEEE International Symposium on Circuits and Systems, ISCAS 2001
Country/Territory	Australia
City	Sydney, NSW
Period	01/5/6 → 01/5/9

ASJC Scopus subject areas

Hardware and Architecture
Electrical and Electronic Engineering
Electronic, Optical and Magnetic Materials

Access to Document

10.1109/ISCAS.2001.921174

Cite this

Abe, M., & Kawamata, M. (2001). Comparison of convergence behavior of distributed evolutionary digital filters. In ISCAS 2001 - 2001 IEEE International Symposium on Circuits and Systems, Conference Proceedings (pp. 729-732). [921174] (ISCAS 2001 - 2001 IEEE International Symposium on Circuits and Systems, Conference Proceedings; Vol. 2). https://doi.org/10.1109/ISCAS.2001.921174

Comparison of convergence behavior of distributed evolutionary digital filters. / Abe, Masahide; Kawamata, Masayuki.
ISCAS 2001 - 2001 IEEE International Symposium on Circuits and Systems, Conference Proceedings. 2001. p. 729-732 921174 (ISCAS 2001 - 2001 IEEE International Symposium on Circuits and Systems, Conference Proceedings; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abe, M & Kawamata, M 2001, Comparison of convergence behavior of distributed evolutionary digital filters. in ISCAS 2001 - 2001 IEEE International Symposium on Circuits and Systems, Conference Proceedings., 921174, ISCAS 2001 - 2001 IEEE International Symposium on Circuits and Systems, Conference Proceedings, vol. 2, pp. 729-732, 2001 IEEE International Symposium on Circuits and Systems, ISCAS 2001, Sydney, NSW, Australia, 01/5/6. https://doi.org/10.1109/ISCAS.2001.921174

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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.",

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AB - 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.

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Y2 - 6 May 2001 through 9 May 2001

ER -

Comparison of convergence behavior of distributed evolutionary digital filters

Abstract

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ASJC Scopus subject areas

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