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Turkish Journal of Electrical Engineering and Computer Sciences

DOI

10.3906/elk-1410-107

Abstract

In this paper, a population-based robust enhanced teaching--learning-based optimization (ETLBO) algorithm with reduced computational effort and high consistency is applied to design stable digital infinite-impulse response (IIR) filters in a multiobjective framework. Furthermore, a decision-making methodology based on fuzzy set theory is applied to handle nonlinear and multimodal design problems of the IIR digital filter. The original teaching--learning-based optimization (TLBO) algorithm has been remodeled by merging the concepts of opposition-based learning and migration for the selection of good candidates and to maintain diversity, respectively. A multiobjective IIR digital filter design problem takes into consideration magnitude and phase response of the filter simultaneously, while satisfying stability constraints on the coefficients of the filter. The order of the filter is controlled by a control gene whose value is also along with filter coefficients, to obtain the optimum order of the designed IIR filter. Results illustrate that ETLBO is more capable and efficient in comparison to other optimization methods for the design of all types of filter, i.e. high-pass, low-pass, band-stop, and band-pass IIR digital filters.

Keywords

Digital infinite impulse response filters, teaching-learning-based optimization, magnitude response, phase response, filter order

First Page

4042

Last Page

4062

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