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Efficient Design of Fixed Point Digital FIR Filters by Using Differential Evolution Algorithm

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Computational Intelligence and Bioinspired Systems (IWANN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3512))

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Abstract

Differential Evolution (DE) algorithm is a new heuristic approach which has been proposed particulary for numeric optimization problems. It is a population based algorithm like genetic algorithms using the similar operators; crossover, mutation and selection. In this work, DE algorithm has been applied to the design of fixed point digital Finite Impuls Response (FIR) filters and its performance has been compared to that of Genetic Algorithm (GA) and Least Squares Algorithm (LSQ).

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References

  1. Chen, S.: IIR Model Identification Using Batch-Recursive Adaptive Simulated Annealing Algorithm. In: Proceed. of 6th Annual Chinese Automation and Computer Science Conf., pp. 151–155 (2000)

    Google Scholar 

  2. Mastorakis, N.E., Gonos, I.F., Swamy, M.N.S.: Design of Two Dimensional Recursive Filters Using Genetic Algorithms. IEEE Transaction on Circ. and Syst. I-Fundamental Theory and Applications 50, 634–639 (2003)

    Article  MathSciNet  Google Scholar 

  3. Storn, R., Price, K.: Differential Evolution - A Simple and Efficient Adaptive Scheme for Global Optimization over Continious Spaces. Technical Report TR - 95 - 012, ICSI (1995), ftp.icsi.berkeley.edu

  4. Storn, R.: Differential evolution design of an IIR- filter with requariments for magnitude and group delay. In: IEEE Int. Conf. on Evolutionary Computation, Japan, pp. 268-273 (1996)

    Google Scholar 

  5. Karaboga, N.: Digital IIR Filter Design using Differential Evolution Algorithm. EUROSIP J. on Applied Sig. Process. (2004) (to be appear)

    Google Scholar 

  6. Karaboğa, N., Çetinkaya, B.: Performance Comparison of Genetic and Differential Evolution Algorithms for Digital FIR Filter Design. In: Yakhno, T. (ed.) ADVIS 2004. LNCS, vol. 3261, pp. 482–489. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  7. Karaboğa, N., Çetinkaya, B.: Performance Comparison of Genetic Algorithm based Design Methods of Digital Filters with Optimal Magnitude Response and Minimum Phase. In: The 46th IEEE Midwest Sym. on Circ. and Syst. (2003) (accepted, in Press)

    Google Scholar 

  8. Lee, A., Ahmadi, M., Jullien, G.A., Miller, W.C., Lashkari, R.S.: Design of 1-D FIR Filters with Genetic Algorithms. In: IEEE Int. Symp. on Circ. and Syst., pp. 295-298 (1999)

    Google Scholar 

  9. Xiaomin, M., Yixian, Y.: Optimal Design of FIR Digital Filter using Genetic Algorithm. The J. China Univ. Posts Telecom. 5, 12–16 (1998)

    Google Scholar 

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Karaboğa, N., Çetinkaya, B. (2005). Efficient Design of Fixed Point Digital FIR Filters by Using Differential Evolution Algorithm. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_99

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  • DOI: https://doi.org/10.1007/11494669_99

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26208-4

  • Online ISBN: 978-3-540-32106-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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