Abstract
This chapter presents the iterative design of finite impulse response (FIR) filters using particle swarm optimization with a quantum infusion (PSO-QI) algorithm. Filter design, in this work, is formulated as a parameter optimization problem using population-based stochastic methods; and hence, it is iterative. PSO-QI is a hybrid algorithm combining PSO and quantum-behaved PSO. PSO-QI combines the best features of these individual algorithms. Therefore, the design specification for FIR filters can be satisfied more accurately. Two methods of evaluating the performance of the algorithm (cost function) are implemented. Minimizing the mean squared error between the actual and the ideal filter response is one approach to performance evaluation. The second approach involves minimizing the mean squared error between the ripples in the passband and the stopband of the designed filter and the desired filter specification. The results presented show that filters designed using PSO-QI most closely match the design specification, and their performance is more consistent when compared with other evolutionary algorithms. The results are compared with the constrained least squares method of filter design.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ababneh, J., Bataineh, M.: Linear phase FIR filter design using particle swarm optimization and genetic algorithms. Digit. Signal Process. 18(4), 657–668 (2008)
Aktan, M., Yurdakul, A., Dundar, G.: An algorithm for the design of low-power hardware-efficient FIR filters. IEEE Trans. Circuits Syst. I, Regul. Pap. 55(6), 1536–1545 (2008)
Chang, C.H., Chen, J., Vinod, A.: Information theoretic approach to complexity reduction of FIR filter design. IEEE Trans. Circuits Syst. I, Regul. Pap. 55(8), 2310–2321 (2008)
Del Valle, Y., Venayagamoorthy, G., Mohagheghi, S., Hernandez, J., Harley, R.: Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans. Evol. Comput. 12(2), 171–195 (2008)
Fang, W., Sun, J., Xu, W.: Analysis of adaptive IIR filter design based on quantum-behaved particle swarm optimization. In: The Sixth World Congress on Intelligent Control and Automation (WCICA 2006), vol. 1 (2006)
Fang, W., Sun, J., Xu, W., Liu, J.: FIR digital filters design based on quantum-behaved particle swarm optimization. In: First International Conference on Innovative Computing, Information and Control (ICICIC’06), vol. 1 (2006)
Huang, W., Zhou, L., Qian, J.: FIR filter design: frequency sampling filters by particle swarm optimization algorithm. In: Proceedings of 2004 International Conference on Machine Learning and Cybernetics, vol. 4 (2004)
Jou, Y., Chen, F.: Least-squares design of FIR filters based on a compacted feedback neural network. IEEE Trans. Circuits Syst. II, Express Briefs 54(5), 427–431 (2007)
Karaboga, N.: Digital IIR filter design using differential evolution algorithm. EURASIP J. Appl. Signal Process. 2005(8), 1269–1276 (2005)
Krusienski, D., Jenkins, W.: Particle swarm optimization for adaptive IIR filter structures. In: Proc. Congress on Evolutionary Computation (CEC2004), vol. 1, pp. 965–970 (2004)
Kwan, H.K., Jiang, A.: FIR, Allpass, and IIR variable fractional delay digital filter design. IEEE Trans. Circuits Syst. I, Regul. Pap. 56(9), 2064–2074 (2009)
Liu, Y., Lin, Z.: Optimal design of frequency-response masking filters with reduced group delays. IEEE Trans. Circuits Syst. I, Regul. Pap. 55(6), 1560–1570 (2008)
Luitel, B., Venayagamoorthy, G.: Differential evolution particle swarm optimization for digital filter design. In: Proc. IEEE World Congress on Computational Intelligence (WCCI’08), pp. 3954–3961 (2008)
Luitel, B., Venayagamoorthy, G.K.: Particle swarm optimization with quantum infusion for system identification. Eng. Appl. Artif. Intell. 23(5), 635–649 (2010)
Mandal, S., Ghoshal, S.P., Kar, R., Mandal, D.: Design of optimal linear phase FIR high pass filter using craziness based particle swarm optimization technique. J. King Saud Univ, Comput. Inf. Sci. 24(1), 83–92 (2012)
Meher, P.K.: New approach to look-up-table design and memory-based realization of FIR digital filter. IEEE Trans. Circuits Syst. I, Regul. Pap. 57(3), 592–603 (2010). doi:10.1109/TCSI.2009.2026683
Shyu, J.J., Pei, S.C., Huang, Y.D.: Design of variable two-dimensional FIR digital filters by McClellan transformation. IEEE Trans. Circuits Syst. I, Regul. Pap. 56(3), 574–582 (2009)
Storn, R.: Designing nonstandard filters with differential evolution. IEEE Signal Process. Mag. 22(1), 103–106 (2005)
Sun, J., Feng, B., Xu, W.: Particle swarm optimization with particles having quantum behavior. In: Proc. Congress on Evolutionary Computation (CEC2004), vol. 1, pp. 325–331 (2004)
Yu, Y., Lim, Y.: Design of linear phase FIR filters in subexpression space using mixed integer linear programming. IEEE Trans. Circuits Syst. I, Regul. Pap. 54(10), 2330–2338 (2007)
Acknowledgements
The financial support provided by NSF EFRI (#1238097) and NSF CAREER (#1231820) is gratefully acknowledged.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Luitel, B., Venayagamoorthy, G.K. (2013). Iterative Design of FIR Filters. In: Chatterjee, A., Nobahari, H., Siarry, P. (eds) Advances in Heuristic Signal Processing and Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37880-5_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-37880-5_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37879-9
Online ISBN: 978-3-642-37880-5
eBook Packages: Computer ScienceComputer Science (R0)