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http://hdl.handle.net/10397/5633
| 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 |
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|---|---|---|---|---|
| Zhu_IIR_Filters_Algorithm_Probabilistic.pdf | 235.42 kB | Adobe PDF | View/Open |
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