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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 4/2015

01.08.2015 | Original Article

Optimal selection of components value for analog active filter design using simplex particle swarm optimization

verfasst von: Bishnu Prasad De, R. Kar, D. Mandal, S. P. Ghoshal

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 4/2015

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Abstract

The simplex particle swarm optimization (Simplex-PSO) is a swarm intelligent based evolutionary computation method. Simplex-PSO is the hybridization of Nedler–Mead simplex method and particle swarm optimization (PSO) without the velocity term. The Simplex-PSO has fast optimizing capability and high computational precision for high-dimensionality functions. In this paper, Simplex-PSO is employed for selection of optimal discrete component values such as resistors and capacitors for fourth order Butterworth low pass analog active filter and second order State Variable low pass analog active filter, respectively. Simplex-PSO performs the dual task of efficiently selecting the component values as well as minimizing the total design errors of low pass analog active filters. The component values of the filters are selected in such a way so that they become E12/E24/E96 series compatible. The simulation results prove that Simplex-PSO efficiently minimizes the total design error to a greater extent in comparison with previously reported optimization techniques.
Vorheriger Artikel Quantification of side-channel information leaks based on data complexity measures for web browsing
Nächster Artikel Study on the relationship between prefix caching and path stretch ratio

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Metadaten
Titel
Optimal selection of components value for analog active filter design using simplex particle swarm optimization
verfasst von
Bishnu Prasad De
R. Kar
D. Mandal
S. P. Ghoshal
Publikationsdatum
01.08.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 4/2015
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-014-0299-0

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