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Design of Optimized Transition Width Linear Phase FIR Filter using PSO Algorithm with Constriction Factor Approach

by Neha, Ajay Pal Singh
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Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 5 - Number 7
Year of Publication: 2016
Authors: Neha, Ajay Pal Singh
10.5120/cae2016652321

Neha, Ajay Pal Singh . Design of Optimized Transition Width Linear Phase FIR Filter using PSO Algorithm with Constriction Factor Approach. Communications on Applied Electronics. 5, 7 ( Jul 2016), 29-35. DOI=10.5120/cae2016652321

@article{ 10.5120/cae2016652321,
author = { Neha, Ajay Pal Singh },
title = { Design of Optimized Transition Width Linear Phase FIR Filter using PSO Algorithm with Constriction Factor Approach },
journal = { Communications on Applied Electronics },
issue_date = { Jul 2016 },
volume = { 5 },
number = { 7 },
month = { Jul },
year = { 2016 },
issn = { 2394-4714 },
pages = { 29-35 },
numpages = {9},
url = { https://www.caeaccess.org/archives/volume5/number7/634-2016652321/ },
doi = { 10.5120/cae2016652321 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-09-04T19:55:38.776220+05:30
%A Neha
%A Ajay Pal Singh
%T Design of Optimized Transition Width Linear Phase FIR Filter using PSO Algorithm with Constriction Factor Approach
%J Communications on Applied Electronics
%@ 2394-4714
%V 5
%N 7
%P 29-35
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Digital filters are an integral part of today’s electronic industry. Finite Impulse Response (FIR) filters, a type of digital filter have found its use in vast number of applications due to their inherent nature of phase linearity and constant delay. But it has the disadvantage of requirement of large computational power and memory to achieve the same sharpness or cutoff that an Infinite Impulse Response (IIR) filter have. The present article aims on achieving an FIR filter with reduced transition width with the requirement of lesser computational elements. The evolutionary algorithm of Particle Swarm Optimization (PSO) is used for this case. The linear phase FIR filter is designed for high pass (HP) and band pass (BP) case in this article. The simulation results achieved transition width as low as 0.040 (HP) and 0.029 (BP) for the FIR filter designed with order 30 and 40 respectively.

References
  1. Oppenheim, A.V. and Schafer, R.W. (2002). Digital Signal Processing. New Delhi: Pearson Education.
  2. Proakis, J.G. and Manolakis, D.G. (2000). Digital Signal Processing-Principles, Algorithms and Applications. New Delhi: Prentice-Hall.
  3. Harris, F.J. (1978). On the use of windows for harmonic analysis with the discrete Fourier transform. IEEE Proceedings, 66, 51-83.
  4. Rabiner, L.R., Herrmann, O. and Chan, D.S.K. (1973). Practical design rules for optimum finite impulse response of low pass digital filters. Bell System Technical Journal, 52(6), 769–799. Rabiner (1970). An approach to the approximation problem for Nonrecursive digital filters.
  5. Parks, T.W. and McClellan, J.H. (1972). Chebyshev approximation for nonrecursive digital filters with linear phase. IEEE Transactions on Circuit Theory, 19(2), 189–194.
  6. McClellan, J.H., Parks, T.W. and Rabiner, L.R. (1973). A computer program for designing optimum FIR linear phase digital filters. IEEE Transactions on Audio Electro acoustics, 21, 506–526.
  7. Joaquim, M. B. and Lucietto, A.S. (2011). A nearly optimum linear-phase digital FIR filters design. Digital Signal Processing, 21, 690–693.
  8. Lu, H.C. and Tzeng, S.T. (2000). Design of arbitrary FIR log filters by genetic algorithm approach. Signal Processing, 80, 497-505.
  9. Radecki, J., Konrad J. and Dubois, E. (1995). Design of Multidimensional Finite-Word length FIR and IIR Filters by Simulated Annealing. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, 42(6), 424-431.
  10. Karaboga, D., Horrocks, D.H., Karaboga, N. and Kalinli, A. (1997). Designing digital FIR filters using Tabu search algorithm. IEEE International Symposium on Circuits and Systems, 4, 2236-2239.
  11. Karaboga, N. (2009). A new design method based on artificial bee colony algorithm for digital IIR filters. Journal of the Franklin Institute, 4, 328–348.
  12. Karaboga, N. and Cetinkaya, B. (2006). Design of Digital FIR Filters Using Differential Evolution Algorithm. Circuits System Signal Processing, 25, 649-660D.
  13. Kennedy, J. and Eberhart, R. (1995). Particle Swarm Optimization. Proceedings of IEEE International Conference on Neural Networks, 4, 1942-1948, Perth.
  14. Fang, W., Sun, J., Xu, W. and Liu, J. (2006). FIR Digital Filters Design Based on Quantum-behaved Particle Swarm Optimization. First International Conference on Innovative Computing, Information and Control, 615-619, Beijing.
  15. Luitel, B. and Venayagamoorthy, G.K. (2008). Differential Evolution Particle Swarm Optimization for Digital Filter Design. IEEE Congress on Evolutionary Computation, 3954-3961, Hong Kong.
  16. Liu, G., Li, Y.X. and He, G. (2010). Design of Digital FIR Filters Using Differential Evolution Algorithm Based on Reserved Gene. IEEE Congress on Evolutionary Computation, 1-7, Barcelona.
  17. Mondal, S., Chakraborty, D., Kar, R., Mandal, D. and Ghoshal, S.P. (2012). Novel Particle Swarm Optimization for High Pass FIR filter Design. IEEE Symposium on Humanities, Science and Engineering Research, 413-418, Kuala Lumpur.
  18. Neha and Singh, A.P. (2014). Design of Linear Phase Low Pass FIR Filter using Particle Swarm Optimization Algorithm. International Journal of Computer Applications, 98(3), 40-44.
  19. Clerc, M. and Kennedy, J. (2002). The particle swarm—explosion, stability, and convergence in a multidimensional complex space. IEEE Transaction on Evolutionary Computation, 6(1), 58–73.
Index Terms

Computer Science
Information Sciences

Keywords

Convergence FIR filter PSO Transition width.

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