Design of Digital IIR Filter with Conflicting Objectives


(*) Corresponding author


Authors' affiliations


DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)

Abstract


Infinite impulse response (IIR) filter design is a highly constrained multiobjective optimization problem involving conflicting objectives. In this paper, particle swarm optimization (PSO) is purposed to efficiently and effectively handle multiple conflicting objectives of IIR filter design. The weighted sum approach is used to minimize magnitude response and phase response of digital IIR filter simultaneously. The value of weights are searched using PSO along with filter coefficients thus assigning different weight vector to each individual particle. The order of the filter is controlled by a control gene whose value is also optimized along with the filter coefficients to obtain optimum order of designed IIR filter. The proposed algorithm shows competitive results with improved diversity and convergence
Copyright © 2013 Praise Worthy Prize - All rights reserved.

Keywords


Digital Infinite-Impulse Response Filters; Multi-Objective Optimization; Particle Swarm Optimization; Lowest Order; Magnitude Response; Phase Response

Full Text:

PDF


References


J. G. Proakis and D. G. Manolakis, Digital Signal Processing: Principles, Algorithms, and Applications (4th Ed., Pearson Education, Inc., New Delhi, 2007).

A. Antoniou, Digital Signal Processing: Signals, Systems and Filters (U.S.A.: McGraw Hill, 2006).

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, Sep. 2000, pp. 351-354, Tampere, Finland.

S.P. Harris and E.C. Ifeachor, Automatic design of frequency sampling filters by hybrid genetic algorithm techniques, IEEE Trans. Signal Process., vol. 46, no. 12, Dec. 1998, pp. 3304–3314.

K.S. Tang, K.F. Man, S. Kwong and Z.F. Liu, Design and optimization of IIR filter structure using hierarchical genetic algorithms, IEEE Trans. Ind. Elecron., vol. 45, no. 3, June 1998, pp. 481–487.

G. Vanuytsel, P. Boets, L.V. Biesen and S. Temmerman, Efficient hybrid optimization of fixed-point cascaded IIR filter coefficients, in Proc. IEEE Int. Conf. Instrum. Meas. Technol., Anchorage, AK, 2002, pp.793–797.

G.X. Zhang, W.D. Jin, and F. Jin, Multi-criterion satisfactory optimization method for designing IIR digital filters, in Proc. Int. Conf. Commun. Technol., Beijing, China, 2003, pp. 1484–1490.

Y. Liu, S.Q. Shao, H. Zhao, X.F. Liao, and J.B. Yu, An application of genetic algorithms with guiding strategy in the design of digital filters, in Proc. Int. Conf. Commun., Circuits Syst., Chengdu, China, 2004, pp. 1141–1145.

Nikos E. Mastorakis, Ioannis F. Gonos and M. N. S. Swamy, Design of Two-Dimensional Recursive Filters Using Genetic Algorithms, IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 50, no.5, May 2003, pp. 634-639.

M. Nilsson, M. Dahl, and I. Claesson, Digital filter design of IIR filters using real valued genetic algorithm, WSEAS Trans. Circuit Syst., vol. 3,no. 1, pp. 29–34, Jan. 2004.

J. Sun, W.B. Xu, and B. Feng, A global search strategy of quantum behaved particle swarm optimization, in Proc. IEEE Conf. Cybernetics Intell. Syst., Singapore, 2004, pp. 111–116.

Chaohua Dai, Weirong Chen and Yunfang Zhu, Seeker optimization algorithm for digital IIR filter design, IEEE Trans. Ind. Electron., vol. 57, no. 5, May 2006, pp. 1710–1718.

S. Chen, R. H. Istepanian, and B. L. Luk, Digital IIR filter design using adaptive simulated annealing, J. Digital Signal Process., vol. 11, no. 3, July 2001,pp. 241–251.

A. Kalinli and N. Karaboga, A new method for adaptive IIR filter design based on Tabu search algorithm, Int. J. Electron. Commun. (AEÜ), vol. 59, no. 2, May 2005, pp. 111–117.

N. Karaboga, A. Kalinli, and D. Karaboga, Designing IIR filters using ant colony optimisation algorithm, J. Engg. Appl. Artificial Intell., vol. 17, no. 3, Apr. 2004, pp. 301–309.

J.-T. Tsai, J.-H. Chou, T.-K. Liu, Optimal design of digital IIR filters by using hybrid taguchi genetic algorithm, IEEE Trans. Ind. Electron. vol. 53, no. 3, June 2006, pp.867–879.

J.-T. Tsai and J.-H.Chou, Optimal design of digital IIR filters by using an improved immune algorithm, IEEE Trans. Signal Process., vol. 54, no. 12, Dec. 2006, pp. 4582–4596.

Y.Yu and Y. Xinjie, Cooperative coevolutionary genetic algorithm for digital IIR filter design, IEEE Trans. Ind. Electron., vol. 54, no. 3, June 2007, pp.1311–1318.

Y. Wang, B. Li, and Y. Chen, Digital IIR filter design using multi-objective optimization evolutionary algorithm, Appl. Soft Comput., Vol. 11, no. 2, March 2011, pp. 1851-1857.

K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput., vol. 6, no. 2, April 2002, pp.182–197.

A. G. Deczky, Synthesis of recursive filters using the minimum p-error criterion, IEEE Trans. Audio Electroacoust., vol. 20, no.4, Oct. 1972 pp. 257–263.

I. Jury, Theory and Application of the Z-Transform Method (New York: Wiley, 1964).

M. C. Lang, Least-squares design of IIR filters with prescribed magnitude and phase responses and a pole radius constraint, IEEE Trans.Signal Process., vol. 48, no. 11, Nov. 2000, pp. 3109–3121.

W. S. Lu, Design of stable IIR digital filters with equiripple passbands and peak-constrained least squares stopbands, IEEE Trans. CircuitsSyst. II, Analog Digit. Signal Process., vol. 46, no. 11, Nov. 1999, pp. 1421–1426.

J.-J. Shyu, S.-C. Pei, and C.-H. Chan, Minimax phase error design of allpass variable fractional-delay digital filters by iterative weighted least-squares method, Signal Process., vol. 89, no. 9, Sep. 2009, pp.1774–1781.

J. L. Sullivan and J. W. Adams, PCLS IIR digital filters with simultaneous frequency response magnitude and group delay specifications, IEEE Trans. Signal Process., vol. 46, no. 11, Nov. 1998, pp. 2853–2861.

C. C. Tseng and S. L. Lee, Minimax design of stable IIR digital filter with prescribed magnitude and phase responses, IEEE Trans.Circuits Syst. I, Fundam. Theory Appl., vol. 49, no. 4, Apr. 2002, pp. 547–551.

R. C. Eberhart and J. Kennedy, A new optimizer using particle swarm theory, in Proc. 6th Int. Symp. Micromachine Human Sci., Nagoya, Japan, 1995, pp. 39–43.

J. Kennedy and R. C. Eberhart, Particle swarm optimization, in Proc. IEEE Int. Conf. Neural Networks, 1995, pp. 1942–1948.

C. A. Coello Coello and M. S. Lechuga, MOPSO: A proposal for multiple objective particle swarm optimization, in Proc. 2002 IEEE Congr. Evolutionary Computation, Honolulu, HI, 2002, pp. 1051–1056.

J. E. Fieldsend and S. Singh, A multi-objective algorithm based upon particle swarm optimization, an efficient data structure and turbulence, in Proc. 2002 U.K. Workshop on Computational Intelligence, Birmingham, U.K., 2002, pp. 34–44.

X. Hu, Multiobjective optimization using dynamic neighbourhood particle swarm optimization, in Proc. 2002 IEEE Congr. Evolutionary Computation, Honolulu, HI, 2002.

K. E. Parsopoulos and M. N. Vrahatis, Particle swarm optimization method in multiobjective problems, in Proc. 2002 ACM Symp. Applied Computing (SAC 2002), Madrid, Spain, 2002, pp. 603–607.

S. Mostaghim and J. Teich, Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO), in Proc. IEEE 2003 Swarm Intelligence Symp., 2003, pp. 26–33.

W. F. Leong and G. G. Yen, PSO-based multiobjective optimization with dynamic population size and adaptive local archives, IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 38, no. 5, Oct. 2008, pp. 1270–1293.

M. R. Sierra and C. A. Coello Coello, Improving PSO-based multiobjective optimization using crowding, mutation and ε–dominance, in Proc. Evol. Multi-Criterion Optimization Conf., Guanajuato, Mexico, 2005, pp. 505–519.

Banaei, M.R., Tuning of damping controller parameters using multi-objective PSO algorithm for STATCOM, (2011) International Review of Electrical Engineering (IREE), 6 (1), pp. 300-308.

Shayeghi, H., Ghasemi, A., Economic load dispatch solution using improved time variant MOPSO algorithm considering generator constraints, (2012) International Review of Electrical Engineering (IREE), 7 (2), pp. 4292-4303.

C.A.Coello Coello, G.Toscano Pulido, and M.Salazar Lechuga, Handling multiple objectives with particle swarm optimization, IEEE Trans. Evol. Comput., vol.8, no.3, 2004, pp. 256–279.

Laïfa, A., Boudour, M., Multi-objective particle swarm optimization for FACTS allocation to enhance voltage security, (2009) International Review of Electrical Engineering (IREE), 4 (5), pp. 994-1004.

Yan, N.-N., Fu, Z.-C., Coordinated design of UPFC based on multi-objective particle swarm optimization, (2010) Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control, 38 (8), pp. 43-48.

R. Eberhart and Y. Shi, Particle swarm optimization: Developments,applications and resources, in Proc. IEEE Congr. Evol. Comput., vol. 1, May 2001, pp. 81–86.

X. Hu, Y. Shi, and R. Eberhart, Recent advances in particle swarm, in Proc. IEEE Congr. Evol. Comput., vol. 1, Jun. 2004, pp. 90–97.

M. Clerc and J. Kennedy, The particle swarm-explosion, stability, and convergence in a multidimensional complex space, IEEE Trans. Evol.Comput., vol. 6, no. 1, Feb. 2002, pp. 58–73.

R. Eberhart and Y. Shi, Comparing inertia weights and constriction factors in particle swarm optimization, in Proc. IEEE Congress Evol.Comput., vol. 1, Jul. 2000, pp. 84–88.

F. Bergh and A. Engelbrecht, A cooperative approach to particle swarm optimization, IEEE Trans. Evol. Comput., vol. 8, no. 3, Jun. 2004, pp. 225–239.

Y. Shi and R. Eberhart, A modified particle swarm optimizer, in Proc.IEEE World Congr. Comput. Intell., May 1998, pp. 69–73.

Y. Shi and R. Eberhart, Empirical study of particle swarm optimization, in Proc. IEEE Congr. Evol. Comput., vol. 3, Jul. 1999, vol. 3, pp.1945–1950.

Bo Liu, Ling Wang, Yi-Hui Jin, Fang Tang, De-Xian Huang, Improved particle swarm optimization combined with chaos, Chaos, Solitons & Fractals, vol. 25, no. 5, Sept. 2005, pp 1261-1271.

R.May, Simple mathematical models with very complicated dynamics, Nature, vol.261, June 1976, pp.459–67.

D.P. Kothari and J.S. Dhillon, Power System Optimization (Prentice Hall of India, New Delhi.2004).


Refbacks

  • There are currently no refbacks.


Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize