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IIR Filter design with Craziness based Particle Swarm Optimization Technique

Authors: Suman Kumar Saha, Rajib Kar, Durbadal Mandal, S. P. Ghoshal


This paper demonstrates the application of craziness based particle swarm optimization (CRPSO) technique for designing the 8th order low pass Infinite Impulse Response (IIR) filter. CRPSO, the much improved version of PSO, is a population based global heuristic search algorithm which finds near optimal solution in terms of a set of filter coefficients. Effectiveness of this algorithm is justified with a comparative study of some well established algorithms, namely, real coded genetic algorithm (RGA) and particle swarm optimization (PSO). Simulation results affirm that the proposed algorithm CRPSO, outperforms over its counterparts not only in terms of quality output i.e. sharpness at cut-off, pass band ripple, stop band ripple, and stop band attenuation but also in convergence speed with assured stability.

Keywords: Stability, PSO, IIR Filter, RGA, CRPSO, Evolutionary Optimization Techniques, Low Pass (LP) Filter, Magnitude Response, Pole-Zero Plot

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[1] A. V. Oppenheim and R. W. Buck, Discrete-Time Signal Processing. Englewood Cliffs, NJ: Prentice-Hall, 1999.
[2] J. G Proakis and D. G. Manolakis, Digital Signal Processing. Englewood Cliffs, NJ: Prentice-Hall, 1996.
[3] S. Das and A. Konar, "A swarm intelligence approach to the synthesis of two-dimensional IIR filters," Engineering Applications of Artificial Intelligence, vol. 20, no. 8, pp. 1086-1096, April 2007.
[4] Z. M. Hussain, A. Z. Sadik and P. O- Shea, Digital Signal Processing- An Introduction with MATLAB Applications. New York: Springer-Verlag, 2011.
[5] R. K. Livesley, Mathematical methods for Engineer. Ellis Horwood Limited, West Sussex, 1989.
[6] L.B. Jackson and G. J. Lemay, "A simple remez exchange algorithm to design IIR filters with zeros on the unit circle," IEEE International Conference on Acoustics, Speech, and Signal Processing, Albuquerque, NM, USA, vol. 3, pp. 1333-1336, 1990.
[7] A. Antoniou, Digital Signal Processing: Signals, Systems and Filters. U.S.A.: McGraw Hill, 2006.
[8] W. S. Lu and A. Antoniou, "Design of digital filters and filter banks by optimization: a state of the art review," in Proc. European Signal Processing Conf., vol. 1, pp. 351-354, Tampere, Finland, Sep. 2000.
[9] J. H. Holland, Adaptation in Natural and Artificial Systems, Ann Arbor, MI: Univ. Michigan Press. 1975.
[10] D. T. Pham and D. Karaboga, Intelligent Optimization Techniques, Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks. New York: Springer-Verlag, 2000.
[11] Z. Michalewics, Genetic Algorithm + Data Structures = Evolution Programs. 2nd ed. New York: Springer - Verlag, 1994.
[12] S. Kirkpatrick, C. D. Gelatt, M. P. Vecchi, "Optimization by simulated annealing," Science, vol. 220, no. 4598, pp. 671-680, 1983.
[13] J. D. Farmer, N. H. Packard and A. S. Perelson, "The immune system, adaptation and machine learning," in Proc. 5th Annu. Int. Conf. Physica D: Nonlinear Phenomena, North - Holland, Amsterdam, 1986, vol. 22, Issues 1-3, pp. 187-204.
[14] M. Dorigo, V. Maniezzo and A. Colorni, "The ant system: optimization by a colony of cooperative agents," IEEE Trans. on Sys., Man and Cybernetics - Part B, vol. 26, no.1, pp. 29-41, 1996.
[15] V. Gazi and K. M. Passino, "Stability analysis of social foraging swarms," IEEE Transactions on Systems, Man and Cybernetics- Part B, vol. 34, no. 1, pp. 539-557, 2004.
[16] D. H. Kim, A. Abraham and J. H. Cho, "A hybrid genetic algorithm and bacterial foraging approach for global optimization," Information Sciences, vol. 177, pp. 3918-3937, 2007.
[17] T. Y. Sun, C-C. Liu, T-Y. Tsai, and S-T. Hsieh, "Adequate determination of a band of wavelet threshold for noise cancellation using particle swarm optimization," in Proc. Evolutionary Computation, 2008, Hong Kong, China, 1-6 June, pp. 1168-1175.
[18] W. Yao, S. Chen, S. Tan and L. Hanzo, "Particle swarm optimization aided minimum bit error rate multi-user transmission," in Proc. IEEE Int. Conf. on Communications, Germany, pp. 1-5, 2009.
[19] D. Mondal, S. P. Ghosal and A. K. Bhattacharya, "Radiation pattern optimization for concentric circular antenna array with central element feeding using craziness based particle swarm optimization," International Journal of RF and Microwave Computer-Aided Engineering, vol. 20, no. 5, pp. 577-586, John Wiley and sons, Inc., Sept. 2010.
[20] D. Mandal, S. P. Ghoshal and A. K. Bhattacharya, "Application of evolutionary optimization techniques for finding the optimal set of concentric circular antenna array," Expert Systems with Applications, (Elsevier), vol. 38, pp. 2942-2950, 2010.
[21] J. Kennedy and R. Eberhart, "Particle swarm optimization", in Proc. IEEE Int. Conf. on Neural Network, vol. 4, pp. 1942-1948, Australia 1995.
[22] R. Eberhart and Y. Shi, "Comparison between genetic algorithm and particle swarm optimization," in Proc. 7th Annu. Conf. Evolutionary Computation, San Diego. 2000.
[23] W. Fang, J. Sun and W. Xu, "A mutated quantum-behaved particle swarm optimizer for digital IIR filter design," EURASIP Journal on Advances in Signal Processing, Article ID-367465, pp. 1-7, 2009.
[24] S. H. Ling, H. H. C. Iu, F. H. F. Leung and K. Y. Chan, "Improved hybrid particle swarm optimized wavelet neural network for modeling the development of fluid dispensing for electronic packaging," IEEE Trans. Ind. Electron., vol. 55, no. 9, pp. 3447- 3460, Sep. 2008.
[25] B. Biswal, P. K. Dash and B. K. Panigrahi, "Power quality disturbance classification using fuzzy c-means algorithm and adaptive particle swarm optimization," IEEE Trans. Ind. Electron., vol. 56, no. 1, pp. 212-220, Jan. 2009.
[26] N. E. Mastorakis, I. F. Gonos, and M. N. S. Swamy, "Design of two dimensional recursive filters using genetic algorithms," IEEE Transaction Circuits and Systems 1- Fundamental Theory and Applications, vol. 50, issue 5, pp. 634-693, May 2003.
[27] A. Ratnaweera, S. K. Halgamuge and H. C. Watson, "Self organizing hierarchical particle swarm optimizer with time varying acceleration coefficients," IEEE Trans. Evolutionary Computational, vol. 8, no. 3, pp.240-255, 2004.
[28] S. M. Guru, S. K. Halgamuge and S. Fernando, "Particle swarm optimizers for cluster formation in wireless sensor networks," in Proc. Int. Conf. on Intelligent Sensors, Sensor Networks and Information Processing, Melbourne, pp. 319-324, 2005.
[29] J. Sun, W-B Xu and J. Liu, "Training RBF neural network via quantum-behaved particle swarm optimization," in Proc. ICONIP 2006, Hong Kong, China, 3-6 Oct. pp. 1156-1163, 2006.
[30] H-M. Feng, "Self-generation RBFNs using evolutional PSO learning," Neuro Computing, vol. 70, nos. 1-3, pp. 41-251, 2006.
[31] K. E. Parsopoulos and M. N. Vrahatis, "Particle swarm optimization and intelligence: Advances and Applications," Information Science Reference, Hershey, New York, 2010.
[32] D. Mandal, S.P. Ghoshal, and A. K. Bhattacharjee, "Radiation Pattern Optimization for Concentric Circular Antenna Array With Central Element Feeding Using Craziness Based Particle Swarm Optimization," International Journal of RF and Microwave Computer-Aided Engineering, John Wiley & Sons, Inc., vol. 20, Issue. 5, pp. 577-586, Sept. 2010.
[33] B. Luitel and G. K. Venayagamoorthy, "Particle swarm optimization with quantum infusion for the design of digital filters," IEEE Swarm Intelligence Symposium, St. Louis MO USA, pp. 1-8, Sep. 2008.