Digital IIR filters design using differential evolution algorithm with a controllable probabilistic population size.

Design of a digital infinite-impulse-response (IIR) filter is the process of synthesizing and implementing a recursive filter network so that a set of prescribed excitations results a set of desired responses. However, the error surface of IIR filters is usually non-linear and multi-modal. In order...

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Autores principales:	Wu Zhu, Jian-an Fang, Yang Tang, Wenbing Zhang, Wei Du
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/afb0322ccd6749e4a0981e2503421652
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id	oai:doaj.org-article:afb0322ccd6749e4a0981e2503421652
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spelling	oai:doaj.org-article:afb0322ccd6749e4a0981e25034216522021-11-18T07:12:45ZDigital IIR filters design using differential evolution algorithm with a controllable probabilistic population size.1932-620310.1371/journal.pone.0040549https://doaj.org/article/afb0322ccd6749e4a0981e25034216522012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22808191/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Design of a digital infinite-impulse-response (IIR) filter is the process of synthesizing and implementing a recursive filter network so that a set of prescribed excitations results a set of desired responses. However, the error surface of IIR filters is usually non-linear and multi-modal. In order to find the global minimum indeed, an improved differential evolution (DE) is proposed for digital IIR filter design in this paper. The suggested algorithm is a kind of DE variants with a controllable probabilistic (CPDE) population size. It considers the convergence speed and the computational cost simultaneously by nonperiodic partial increasing or declining individuals according to fitness diversities. In addition, we discuss as well some important aspects for IIR filter design, such as the cost function value, the influence of (noise) perturbations, the convergence rate and successful percentage, the parameter measurement, etc. As to the simulation result, it shows that the presented algorithm is viable and comparable. Compared with six existing State-of-the-Art algorithms-based digital IIR filter design methods obtained by numerical experiments, CPDE is relatively more promising and competitive.Wu ZhuJian-an FangYang TangWenbing ZhangWei DuPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 7, p e40549 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Wu Zhu Jian-an Fang Yang Tang Wenbing Zhang Wei Du Digital IIR filters design using differential evolution algorithm with a controllable probabilistic population size.
description	Design of a digital infinite-impulse-response (IIR) filter is the process of synthesizing and implementing a recursive filter network so that a set of prescribed excitations results a set of desired responses. However, the error surface of IIR filters is usually non-linear and multi-modal. In order to find the global minimum indeed, an improved differential evolution (DE) is proposed for digital IIR filter design in this paper. The suggested algorithm is a kind of DE variants with a controllable probabilistic (CPDE) population size. It considers the convergence speed and the computational cost simultaneously by nonperiodic partial increasing or declining individuals according to fitness diversities. In addition, we discuss as well some important aspects for IIR filter design, such as the cost function value, the influence of (noise) perturbations, the convergence rate and successful percentage, the parameter measurement, etc. As to the simulation result, it shows that the presented algorithm is viable and comparable. Compared with six existing State-of-the-Art algorithms-based digital IIR filter design methods obtained by numerical experiments, CPDE is relatively more promising and competitive.
format	article
author	Wu Zhu Jian-an Fang Yang Tang Wenbing Zhang Wei Du
author_facet	Wu Zhu Jian-an Fang Yang Tang Wenbing Zhang Wei Du
author_sort	Wu Zhu
title	Digital IIR filters design using differential evolution algorithm with a controllable probabilistic population size.
title_short	Digital IIR filters design using differential evolution algorithm with a controllable probabilistic population size.
title_full	Digital IIR filters design using differential evolution algorithm with a controllable probabilistic population size.
title_fullStr	Digital IIR filters design using differential evolution algorithm with a controllable probabilistic population size.
title_full_unstemmed	Digital IIR filters design using differential evolution algorithm with a controllable probabilistic population size.
title_sort	digital iir filters design using differential evolution algorithm with a controllable probabilistic population size.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/afb0322ccd6749e4a0981e2503421652
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Digital IIR filters design using differential evolution algorithm with a controllable probabilistic population size.

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