Design of FIR filters with differential evolution

Guangquan Zhao; Xiyuan Peng

doi:10.1109/ICEMI.2007.4350559

Design of FIR filters with differential evolution

Guangquan Zhao^*
, Xiyuan Peng

^*Corresponding author for this work

School of Electronics and Information Engineering

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

3 Scopus citations

Abstract

Frequency sampling method is one of the usual methods in FIR digital filter design. In frequency sampling method the value of transition band samples are usually obtained by searching for table. However, the value obtained by searching for table can not be ensured to be optimal. In this paper a new application of Differential Evolution (DE) to frequency sampling method is introduced and its performance is compared to that of Genetic Algorithm(GA). Experimental results have shown that the value of transition band samples obtained by DE can be ensured to be optimal, also, it is seen that DE is significantly faster than GA for finding the optimum filter.

Original language	English
Title of host publication	2007 8th International Conference on Electronic Measurement and Instruments, ICEMI
Pages	1748-1751
Number of pages	4
DOIs	https://doi.org/10.1109/ICEMI.2007.4350559
State	Published - 2007
Event	2007 8th International Conference on Electronic Measurement and Instruments, ICEMI - Xian, China Duration: 16 Aug 2007 → 18 Aug 2007

Publication series

Name	2007 8th International Conference on Electronic Measurement and Instruments, ICEMI

Conference

Conference	2007 8th International Conference on Electronic Measurement and Instruments, ICEMI
Country/Territory	China
City	Xian
Period	16/08/07 → 18/08/07

Keywords

Differential evolution
FIR filter
Frequency sampling method

Access to Document

10.1109/ICEMI.2007.4350559

Cite this

@inproceedings{50224725e9a84ac1a16e07a000a07ce0,

title = "Design of FIR filters with differential evolution",

abstract = "Frequency sampling method is one of the usual methods in FIR digital filter design. In frequency sampling method the value of transition band samples are usually obtained by searching for table. However, the value obtained by searching for table can not be ensured to be optimal. In this paper a new application of Differential Evolution (DE) to frequency sampling method is introduced and its performance is compared to that of Genetic Algorithm(GA). Experimental results have shown that the value of transition band samples obtained by DE can be ensured to be optimal, also, it is seen that DE is significantly faster than GA for finding the optimum filter.",

keywords = "Differential evolution, FIR filter, Frequency sampling method",

author = "Guangquan Zhao and Xiyuan Peng",

year = "2007",

doi = "10.1109/ICEMI.2007.4350559",

language = "英语",

isbn = "1424411351",

series = "2007 8th International Conference on Electronic Measurement and Instruments, ICEMI",

pages = "1748--1751",

booktitle = "2007 8th International Conference on Electronic Measurement and Instruments, ICEMI",

note = "2007 8th International Conference on Electronic Measurement and Instruments, ICEMI ; Conference date: 16-08-2007 Through 18-08-2007",

}

Zhao, G & Peng, X 2007, Design of FIR filters with differential evolution. in 2007 8th International Conference on Electronic Measurement and Instruments, ICEMI., 4350559, 2007 8th International Conference on Electronic Measurement and Instruments, ICEMI, pp. 1748-1751, 2007 8th International Conference on Electronic Measurement and Instruments, ICEMI, Xian, China, 16/08/07. https://doi.org/10.1109/ICEMI.2007.4350559

Design of FIR filters with differential evolution. / Zhao, Guangquan ; Peng, Xiyuan.
2007 8th International Conference on Electronic Measurement and Instruments, ICEMI. 2007. p. 1748-1751 4350559 (2007 8th International Conference on Electronic Measurement and Instruments, ICEMI).

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

TY - GEN

T1 - Design of FIR filters with differential evolution

AU - Zhao, Guangquan

AU - Peng, Xiyuan

PY - 2007

Y1 - 2007

N2 - Frequency sampling method is one of the usual methods in FIR digital filter design. In frequency sampling method the value of transition band samples are usually obtained by searching for table. However, the value obtained by searching for table can not be ensured to be optimal. In this paper a new application of Differential Evolution (DE) to frequency sampling method is introduced and its performance is compared to that of Genetic Algorithm(GA). Experimental results have shown that the value of transition band samples obtained by DE can be ensured to be optimal, also, it is seen that DE is significantly faster than GA for finding the optimum filter.

AB - Frequency sampling method is one of the usual methods in FIR digital filter design. In frequency sampling method the value of transition band samples are usually obtained by searching for table. However, the value obtained by searching for table can not be ensured to be optimal. In this paper a new application of Differential Evolution (DE) to frequency sampling method is introduced and its performance is compared to that of Genetic Algorithm(GA). Experimental results have shown that the value of transition band samples obtained by DE can be ensured to be optimal, also, it is seen that DE is significantly faster than GA for finding the optimum filter.

KW - Differential evolution

KW - FIR filter

KW - Frequency sampling method

UR - https://www.scopus.com/pages/publications/51349091484

U2 - 10.1109/ICEMI.2007.4350559

DO - 10.1109/ICEMI.2007.4350559

M3 - 会议稿件

AN - SCOPUS:51349091484

SN - 1424411351

SN - 9781424411351

T3 - 2007 8th International Conference on Electronic Measurement and Instruments, ICEMI

SP - 1748

EP - 1751

BT - 2007 8th International Conference on Electronic Measurement and Instruments, ICEMI

T2 - 2007 8th International Conference on Electronic Measurement and Instruments, ICEMI

Y2 - 16 August 2007 through 18 August 2007

ER -

Design of FIR filters with differential evolution

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Keywords

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