International Journal of Fuzzy Logic and Intelligent Systems

  • 제2권2호
  • /
  • Pages.115-121
  • /
  • 2002
  • /
  • 1598-2645(pISSN)
  • /
  • 2093-744X(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
권호 표지
DOI QR코드

DOI QR Code

Design of Optimal Digital IIR Filters using the Genetic Algorithm

  • Jang, Jung-Doo (Department of Electrical Engineering Soongsil University) ;
  • Kang, Seong G. (Department of Electrical Computer Engineering The University of Tennessee)
  • 발행 : 2002.06.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

This paper presents an evolutionary design of digital IIR filters using the genetic algorithm (GA) with modified genetic operators and real-valued encoding. Conventional digital IIR filter design methods involve algebraic transformations of the transfer function of an analog low-pass filter (LPF) that satisfies prescribed filter specifications. Other types of frequency-selective digital fillers as high-pass (HPF), band-pass (BPF), and band-stop (BSF) filters are obtained by appropriate transformations of a prototype low-pass filter. In the GA-based digital IIR filter design scheme, filter coefficients are represented as a set of real-valued genes in a chromosome. Each chromosome represents the structure and weights of an individual filter. GA directly finds the coefficients of the desired filter transfer function through genetic search fur given filter specifications of minimum filter order. Crossover and mutation operators are selected to ensure the stability of resulting IIR filters. Other types of filters can be found independently from the filter specifications, not from algebraic transformations.

키워드

참고문헌

  1. A. V. Oppenheim and R. W. Schafer, Discrete-Time Signal Processing, Prentice Hall, 1989
  2. D. E. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning, Addison Wesley, 1989
  3. Z. Michalewicz, Genetic Algorithms + Data Structures = Evolutionary Programs, Springer-Verlag: Berlin, Heidelberg, 1994
  4. D. M. Etter, M. J. Hicks, and K. H. Cho, 'Recursive Adaptive Filter Design using an Adaptive Genetic Algorithm,' Proc. of IEEE Int. Conf. on ASSP, pp.635-638, 1982
  5. R. Nambiar, C. K. Tang, and P. Mars, 'Genetic and learning automata algorithms for adaptive digital filters,' Proc. IEEE Int. Conf. ASSP, Vol. IV, pp.41-44, 1992
  6. S. J. Flockton and M. S. White, 'Pole-zero identification using genetic algorithms,' Proc. 5th Int. Conf. on Genetic AIgorithms, pp.531-535, 1993
  7. K. S. Tang, K. F. Man, S. Kwong, and Q. He, 'Genetic Algorithms and their Applications,' IEEE SignaI Processing Magazine, pp.22-37, 1996
  8. K. S. Tang, K. F. Man, S. Kwong, and Z. F. Liu, 'Design and Optimization of IIR Filter Structure Using Hierarchical Genetic Algorithms,' IEEE Trans. on Industrial EIectronics, Vol. 45, No. 3, pp.481-487, 1998 https://doi.org/10.1109/41.679006
  9. T. P. Krauss, L. Shure, and J. N. Little, MATLAB Signal Processing Toolbox, Mathworks Inc., 1994
  10. S. Chen, R. Istepanian, and B. L. Luk, 'Digital IIR filter design using adaptive simulated annealing,' DigitaI SignaI Processing, Vol. 11, No. 3, 241-251, July 2001 https://doi.org/10.1006/dspr.2000.0384