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Frequency Domain FIR Filter Design Using Fuzzy Adaptive Simulated Annealing

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Abstract

An alternative approach to digital filter design is presented. The overall technique is as follows: Starting from frequency domain constraints and a parameterized expression of the filter family under adaptation, a corresponding training set is created, an error function is synthesized and a global minimization process is executed. At the end, the point that minimizes globally the particular cost function at hand determines the optimal filter. The adopted numerical optimization algorithm is based upon the well-known simulated annealing paradigm and its implementation is known as fuzzy adaptive simulated annealing. Although it is used in this paper to fit FIR filters to frequency domain specifications, the method is suitable to application in other problems of digital filter design, where the matter under study can be stated as finding the global minimum of a numerical function of filter parameters. Design examples are shown to verify the effectiveness of the proposed approach.

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References

  1. T. Bose, Digital Signal and Image Processing (Wiley, New York, 2004)

    Google Scholar 

  2. C.K. Goh, Y.C. Lim, C.S. Ng, Improved weighted least squares algorithm for the design of quadrature mirror filters. IEEE Trans. Signal Process. 47(7), 1866–1877 (1999)

    Article  Google Scholar 

  3. O. Häggström, Finite Markov Chains and Algorithmic Applications (Cambridge University Press, Cambridge, 2002)

    MATH  Google Scholar 

  4. W.-P. Huang, L.-F. Zhou, J.-X. Qian, FIR filter design: frequency sampling filters by particle optimization algorithm, in Third Int. Conf. on Machine Learning and Cybernetics, Shanghai, Aug. 2004, pp. 26–29

  5. L. Ingber, Adaptive simulated annealing (ASA): Lessons learned. Control Cybern. 25(1), 33–54 (1996)

    MATH  Google Scholar 

  6. L. Ingber, Very fast simulated re-annealing. Math. Comput. Model. 12(8), 967–973 (1989)

    Article  MATH  MathSciNet  Google Scholar 

  7. J.D. Johnston, A filter family designed for use in quadrature mirror filter banks, in IEEE Int. Conf. Acoustics, Speech and Signal Process (1980), pp. 291–294

  8. Y.D. Jou, Design of real FIR filters with arbitrary magnitude and phase specifications using a neural-based approach. IEEE Trans. Circuits Syst. II, Express Briefs 53(10), 1068–1072 (2006)

    Article  Google Scholar 

  9. Y.D. Jou, Least-squares design of digital differentiators using neural networks with closed-form derivations. IEEE Signal Process, Lett. 12(11), 760–763 (2005)

    Article  Google Scholar 

  10. S.S. Kidambi, R.P. Ramachandran, Design of nonrecursive filters satisfying arbitrary magnitude and phase specifications using a least-squares approach. IEEE Trans. Circuits Syst. II, Analog Digit. Signal Process. 42(11), 711–716 (1995)

    Article  Google Scholar 

  11. M.-I. Lai, S.-K. Jeng, Compact microstrip dual-band bandpass filters design using genetic-algorithm techniques. IEEE Trans. Microwave Theory Tech. 54(1), 160–168 (2008)

    Google Scholar 

  12. T. Nguyen, The design of arbitrary FIR digital filters using the eigenfilter method. IEEE Trans. Signal Process. 41(3), 1128–1139 (1993)

    Article  MATH  Google Scholar 

  13. H.A. Oliveira Jr., Fuzzy control of stochastic global optimization algorithms and VFSR. Nav. Res. Mag. 16, 103–113 (2003)

    MathSciNet  Google Scholar 

  14. S.C. Pei, J.J. Shyu, Eigen-approach for designing FIR filters and all-pass phase equalizer with prescribed magnitude and phase response. IEEE Trans. Circuits Syst. II, Analog Digit. Signal Process. 39(3), 137–146 (1992)

    Article  Google Scholar 

  15. J. Pintér, Global Optimization in Action (Kluwer Academic, Dordrecht, 1996)

    MATH  Google Scholar 

  16. T. Ross, Fuzzy Logic with Engineering Applications (McGraw-Hill, New York, 1995)

    MATH  Google Scholar 

  17. T. Schnier, X. Yao, P. Liu, Digital design using multiple Pareto fronts. Soft Comput. 8, 332–343 (2004)

    Google Scholar 

Download references

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Correspondence to Antonio Petraglia.

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Oliveira, H.A., Petraglia, A. & Petraglia, M.R. Frequency Domain FIR Filter Design Using Fuzzy Adaptive Simulated Annealing. Circuits Syst Signal Process 28, 899–911 (2009). https://doi.org/10.1007/s00034-009-9128-1

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  • DOI: https://doi.org/10.1007/s00034-009-9128-1

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