Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/5633
PIRA download icon_1.1View/Download Full Text
Title: Digital IIR filters design using differential evolution algorithm with a controllable probabilistic population size
Authors: Zhu, W
Fang, JA
Tang, Y
Zhang, W
Du, W
Issue Date: 11-Jul-2012
Source: PLoS one, 11 July 2012, v. 7, no. 7, e40549, p. 1-9
Abstract: 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.
Publisher: Public Library of Science
Journal: PLoS one 
EISSN: 1932-6203
DOI: 10.1371/journal.pone.0040549
Rights: © 2012 Zhu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
Zhu_IIR_Filters_Algorithm_Probabilistic.pdf235.42 kBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page views

66
Last Week
0
Last month
Citations as of May 15, 2022

Downloads

116
Citations as of May 15, 2022

SCOPUSTM   
Citations

22
Last Week
0
Last month
0
Citations as of May 19, 2022

WEB OF SCIENCETM
Citations

13
Last Week
0
Last month
0
Citations as of May 19, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.