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Application of evolutionary algorithm to design minimal phase digital filters with non-standard amplitude characteristics and finite bit word length

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper an application of evolutionary algorithm to design minimal phase digital filters with non-standard amplitude characteristics and with finite bit word length is presented. Four digital filters with infinite impulse response were designed using the proposed method. These digital filters possess: linearly falling characteristics, linearly growing characteristics, nonlinearly falling characteristics, and nonlinearly growing characteristics, and they are designed using bit words with an assumed length. This bit word length is connected with a processing register size. This register size depends on hardware possibilities where digital filter is to be implemented. In this paper, a modification of the mutation operator is introduced too. Due to this modification, better results were obtained in relation to the results obtained using the evolutionary algorithm with other mutation operators. The digital filters designed using the proposed method can be directly implemented in the hardware (DSP system) without any additional modifications.
Rocznik
Strony
125--135
Opis fizyczny
Bibliogr. 27 poz., rys., tab.
Twórcy
autor
  • Department of Electronics and Computer Science, Koszalin University of Technology, 2 Śniadeckich St., 75-453 Koszalin, Poland, aslowik@ie.tu.koszalin.pl
Bibliografia
  • [1] R. Lyons, Introduction to Digital Signal Processing, WKL, Warsaw, 2000.
  • [2] A. Slowik and M. Bialko, “Evolutionary design of IIR digital filters with non-standard amplitude characteristics”, Proc. 3-rd Nat. Conf. Electronics 1, 345–350 (2004).
  • [3] M. Erba, R. Rossi, V. Liberali, and A.G. Tettamanzi, “Digital filter design through simulated evolution”, Proc. ECCTD ’01 2, 137–140 (2001).
  • [4] S. Rainer and K.V. Price, “Differential evolution — a simple and efficient heuristic for global optimization over continuous spaces”, J. Global Optimization 11, 341–359 (1997).
  • [5] K.V. Price, “An introduction to differential evolution”, D. Corne, M. Dorigo, and F. Glover, editors, New Ideas in Optimization, pp. 79–108, McGraw-Hill, London, 1999.
  • [6] J. Kennedy, R.C. Eberhart, and Y. Shi, Swarm Intelligence, Morgan Kaufmann Publishers, San Francisco, 2001.
  • [7] D.T. Pham, A. Ghanbarzadeh, E. Ko, S. Otri, S. Rahim, and M. Zaidi, “The bees algorithm – a novel tool for complex optimisation problems”, IPROMS 2006, Intelligent Production, Machines and Systems 1, CD-ROM (2006).
  • [8] A. Slowik and M. Bialko, “Design and optimization of IIR digital filters with non-standard characteristics using continous ant colony optimization algorithm”, 5th Hellenic Conf. on Artificial Intelligence, Lecture Notes in Computer Science 5138, 395–400 (2008).
  • [9] K. Socha and M. Doringo, “Ant colony optimization for continous domains”, Eur. J. Operational Research 185 (3), 1155–1173 (2008).
  • [10] M. Dorigo, V. Maniezzo, and A. Colorni, “Ant system: optimization by a colony of cooperating agents”, IEEE Transactions on SMC-B 26 (1), 29–41 (1996).
  • [11] H.G. Beyer and H.P. Schwefel, “Evolution strategies: a comprehensive introduction”, J. Natural Computing 1 (1), 3–52 (2002).
  • [12] R.L. Becerra and C.A. Coello, “Cultured differential evolution for constrained optimization”, Computer Methods in Applied Mechanics and Engineering 195 (33–36), 4303–4322 (2006).
  • [13] D. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Publishing Company Inc., Boston, 1989.
  • [14] Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, Springer-Verlag, Berlin, 1992.
  • [15] J. Arabas, Lectures on Evolutionary Algorithms, WNT, Warsaw, 2001.
  • [16] N. Benvenuto, M. Marchesi, G. Orlandi, F. Piazza, and A. Uncini, “Finite wordlength digital filter design using an annealing algorithm”, Int. Conf. on Acoustics, Speech, and Signal Processing 2, 861–864 (1989).
  • [17] M. Nakamoto, T. Yoshiya, and T. Hinamoto, “Finite wordlength design for IIR digital filters based on the modified least-square criterion in the frequency domain”, Int. Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 1, 462–465 (2007).
  • [18] N. Karaboga and B. Cetinkaya, “Performance comparison of genetic algorithm based design methods of digital filters with optimal magnitude response and minimum phase”, 46th IEEE Midwest Symposium on Circuits and Systems 1, CD-ROM (2003).
  • [19] N.D. Venkata and B.L. Evans, “Optimal design of real and complex minimum phase digital FIR filters”, IEEE Int. Conf. on Acoustics, Speech, and Signal Processing 1, CD-ROM (1999).
  • [20] S.J. Orfanidis, Introduction to Signal Processing, Prentice-Hall, Englewood Cliffs, 1995.
  • [21] H. Ding, J. Lu, X. Qiu, and B. Xu, “Anadaptive speech enhancement method for siren noise cancellation”, Applied Acoustics 65, 385-–399 (2004).
  • [22] TMS320C54x DSP Library Programmer’s Reference, Texas Instruments, Dallas, 2001.
  • [23] TMS320C55x DSP Library Programmer’s Reference, Texas Instruments, Dallas, 2002.
  • [24] W.S. Gan and S.M. Kuo, “Teaching DSP software development: from design to fixed-point implementations”, IEEE Trans. on Education 49 (1), 122–131 (2006).
  • [25] G.S. Baicher, “Optimization of finite word length coefficient IIR digital filters through genetic algorithms – a comparative study”, Lecture Notes on Computer Science 4222, 641–650 (2006).
  • [26] N. Karaboga and B. Cetinkaya, “Design of minimum phase digital IIR filters by using genetic algorithm”, Proc. 6th Nordic Signal Processing Symposium 1, CD-ROM (2004).
  • [27] A. Slowik, “Evolutionary design of digital filters with nonstandard amplitude characteristics and finite bit word length”, Proc. IX Nat. Conf. on Electronics 1, 255–261 (2010).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPG8-0048-0046
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