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Optimal analog active filter design using craziness‐based particle swarm optimization algorithm
Author(s) -
De Bishnu Prasad,
Kar R.,
Mandal D.,
Ghoshal S. P.
Publication year - 2014
Publication title -
international journal of numerical modelling: electronic networks, devices and fields
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.249
H-Index - 30
eISSN - 1099-1204
pISSN - 0894-3370
DOI - 10.1002/jnm.2040
Subject(s) - particle swarm optimization , analogue filter , component (thermodynamics) , filter (signal processing) , computer science , evolutionary computation , filter design , computation , algorithm , evolutionary algorithm , convergence (economics) , mathematical optimization , control theory (sociology) , mathematics , digital filter , artificial intelligence , control (management) , physics , thermodynamics , economics , computer vision , economic growth
Summary Because of the manufacturing constraints, the optimal selection of passive component values for the design of analog active filter is very critical. As the search on possible combinations in preferred values for capacitors and resistors is an exhaustive process, it has to be automated with high accuracy within short computation time. Evolutionary computation may be an attractive alternative for automatic selection of optimal discrete component values such as resistors and capacitors for analog active filter design. This paper presents an efficient evolutionary optimization approach for optimal analog filter design considering different topologies and manufacturing series by selecting their component values. The evolutionary optimization technique employed is craziness‐based particle swarm optimization (CRPSO). PSO is very simple in concept, easy to implement and computationally efficient algorithm with two main advantages: fast convergence and only a few control parameters. However, the performance of PSO depends on its control parameters and may be influenced by premature convergence and stagnation problem. To overcome these problems, the PSO algorithm has been modified to CRPSO and is used for the selection of optimal passive component values of fourth‐order Butterworth low‐pass analog active filter and second‐order state variable low‐pass filter, respectively. CRPSO performs the dual task of efficiently selecting the component values as well as minimizing the total design errors of low‐pass 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 CRPSO efficiently minimizes the total design error with respect to previously used optimization techniques. Copyright © 2014 John Wiley & Sons, Ltd.