IIR Filter design with Craziness based Particle Swarm Optimization Technique
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.
[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.
[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.
@article{"International Journal of Electrical, Electronic and Communication Sciences:49644", author = "Suman Kumar Saha and Rajib Kar and Durbadal Mandal and S. P. Ghoshal", title = "IIR Filter design with Craziness based Particle Swarm Optimization Technique", abstract = "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 = "IIR Filter, RGA, PSO, CRPSO, Evolutionary Optimization Techniques, Low Pass (LP) Filter, Magnitude
Response, Pole-Zero Plot, Stability.", volume = "5", number = "12", pages = "1625-8", }
