AUTHORS: Mehmet Bahadir Çetinkaya

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ABSTRACT: The theory and design of adaptive finite impulse response (FIR) filters are well developed and widely applied in practice due to their simple analytic description of error surfaces and intrinsic stable behavior. However, the studies on adaptive infinite impulse response (IIR) filters are not as common as adaptive FIR filters. The reason is that there are two main drawbacks in the design of adaptive IIR filters: stability during the adaptation process may not be ensured in some applications and the convergence to the optimal design is not always guaranteed because of their multi-modal error surface structures. In order to overcome these difficulties, global optimization based approaches are used in adaptive IIR filter design. One of the most recently proposed swarm intelligence based global optimization algorithms is the artificial bee colony (ABC) algorithm which simulates the intelligent foraging behavior of honeybee swarms. In this work, a novel approach based on artificial bee colony algorithm is described and applied to the design of adaptive IIR filters and its performance is compared to that of differential evolution (DE) and particle swarm optimization (PSO) algorithms.

KEYWORDS: Artificial bee colony algorithm, Modified artificial bee colony algorithm, Particle swarm optimization algorithm, Differential evolution algorithm, Adaptive IIR filter design, System identification

REFERENCES:

[1] A. Kalinli, N. Karaboga, A new method for adaptive IIR filter design based on tabu search algorithm, International. Journal of Electronics and Communications, Vol.59, No.2, 2004, pp. 1-7.

[2] S. Haykin, Adaptive Filter Theory, Prentice Hall, 2002.

[3] D.J. Krusienski, W.K. Jenkins, Design and performance of adaptive systems based on structured stochastic optimization strategies, IEEE Circuits and Systems Magazine. Vol.5, No.1, 2005, pp. 8-20.

[4] L. Pasquato, İ. Kale, System identification via hybrid FIR-IIR adaptive filtering, IEEE Instrumentation and Measurement Technology Conference, 1999, pp. 1064-1069.

[5] M.J. Bruno, J.E. Cousseau, P.D. Donate, On reduced complexity IIR adaptive filters, IEEE International Symposium on Circuits and Systems, 2005, pp. 4329-4332.

[6] S.C. Ng, S.H. Leung, C.Y. Chung, A. Luk, W.H. Lau, The genetic search approach: A new learning algorithm for adaptive IIR filtering, IEEE Signal Processing Magazine, Vol.13, No.6, 1996, pp. 38-46.

[7] K.S. Tang, K.F. Man, S. Kwong, Q. He, Genetic algorithms and their applications, IEEE Signal Processing Magazine, Vol.13, No.6, 1996, pp. 22-37.

[8] N. Karaboga, Digital IIR filter design using differential evolution algorithm, EURASIP Journal on Advances in Signal Processing, Vol.2005, No.8, 2005, pp. 1269-1276. 0 50 100 150 200 250 300 350 400 450 500 0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009 0.01 Cycle Mean Squared Error (MSE) SNR= 30dB SNR= 20dB SNR= 10dB SNR= 5 dB 0 50 100 150 200 250 300 350 400 450 500 0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009 0.01 Cycle Mean Squared Error (MSE) SNR= 30dB SNR= 20dB SNR= 10dB SNR= 5 dB 50 100 150 200 250 300 350 400 450 500 0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009 0.01 Cycle Mean Squared Error (MSE) SNR= 30dB SNR= 20dB SNR= 10dB SNR= 5 dB 0 50 100 150 200 250 300 350 400 450 500 0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009 0.01 Cycle Mean Squared Error (MSE) SNR= 30dB SNR= 20dB SNR= 10dB SNR= 5 dB

[9] D.J. Krusienski, W.K. Jenkins, Adaptive filtering via particle swarm optimization, 37th Asilomar Conference on Signals Systems and Computers, 2003, pp. 571-575.

[10] D.J. Krusienski, W.K. Jenkins, Particle swarm optimization for adaptive IIR filter structures, Congress on Evolutionary Computation, 2004, pp. 965-970.

[11] S. Chen, B.L. Luk, Adaptive simulated annealing for optimization in signal processing applications, Signal Processing, Vol.79, No.1, 1999, pp. 117-128.

[12] S. Chen, R. Istepanian, B.L. Luk, Digital IIR filter design using adaptive simulated annealing, Signal Processing, Vol.11, No.3, 2001, pp. 241-251.

[13] N. Karaboga, A new design method based on artificial bee colony algorithm for digital IIR filters, Journal of the Franklin Institute, Vol.346, No.4, 2009, pp. 328-348.

[14] D. Karaboga, B. Basturk, On the performance of artificial bee colony (ABC) algorithm, Applied Soft Computing, Vol.8, No.1, 2008, pp. 687-697.

[15] A. P. Engelbrecht, Fundamentals of Computational Swarm Intelligence, John Wiley & Sons Publication, 2005.

[16] M. Dorigo, M. Birattari, Swarm Intelligence, Scholarpedia, Vol.2, No.9, 2007, pp. 1462.

[17] D. Karaboga, An idea based on honey bee swarm for numerical optimization. Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.

[18] D. Karaboga, B. Basturk, A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm, Journal of Global Optimization, Vol.39, No.3, 2007, pp. 459-471.

[19] D. Karaboga, C. Ozturk, A novel clustering approach: Artificial Bee Colony (ABC) algorithm, Applied Soft Computing, Vol.11, No.1, 2011, pp. 652-657.

[20] T. Kurban, E. Besdok, A Comparison of RBF neural network training algorithms for inertial sensor based terrain classification, Sensors, Vol.9, No.8, 2009, pp. 6312-6329.

[21] A. Singh, An artificial bee colony algorithm for the leaf-constraint minimum spanning tree problem, Applied Soft Computing, Vol.9, No.2, 2009, pp. 625-631.

[22] R. S. Rao, S.V.L. Narasimham, M. Ramalingaraju, Optimization of distribution network configuration for loss reduction using artificial bee colony algorithm, International Journal of Electrical Power and Energy Systems Engineering, Vol.1, No.2, 2008, pp. 116-122.

[23] P.W. Tsai, J.S. Pan, B.Y. Liao, S.C. Chu, Interactive artificial bee colony algorithm, International Symposium on Intelligent Informatics, 2008, pp. 247-251.

[24] S. Hemamalini, S.P. Simon, Economic load dispatch with valve-point effect using artificial bee colony algorithm, 32th National Systems Conference, 2008, pp. 17-19.

[25] D. Karaboga, B. Akay, A comparative study of artificial bee colony algorithm, Applied Mathematics and Computation, Vol.214, No.1, 2009, pp. 108-132.

[26] D. Karaboga, B. Basturk, Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems, LNCS: Advances in Soft Computing: Foundations of Fuzzy Logic and Soft Computing, 2007, pp. 789-798.

[27] M.S. White, S. J. Flockton, Evolutionary Algorithms in Engineering Applications, Springer Verlag, 1997.

[28] R. Storn, Differential evolution design of an IIR-filter, Congress on Evolutionary Computation, 1996, pp. 268-273.

[29] B. Akay, D. Karaboga, Parameter tuning for the artificial bee colony algorithm, LNAI: 1st International Conference on Computational Collective Intelligence, 2009, pp. 608-619.