AUTHORS: Takuro Kondo, Yasunori Sugita

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ABSTRACT: This paper presents an adaptive IIR system identification method using Particle Swarm Optimization (PSO). System identification is a method for estimating characteristic of an unknown system using the measured input and output signals. In PSO, potential solutions called particles are updated according to simple mathematical formulas of particle’s positions and velocities. However, the IIR system identification methods using PSO have a problem that it is very difficult to get the global optimum solution when the adaptive filter becomes once unstable during system identification. Moreover, the standard PSO has a problem that it tends to converge to local optimal solution because of its strong directivity. In the proposed method, the particle’s velocities are updated using plural better solutions in order to avoid the convergence to local optimal solution and the output signal of an unknown system is used as the feedback signal of the adaptive filter in order to achieve stable system identification. Some simulation results show that the proposed method has higher identification accuracy than conventional methods.

KEYWORDS: Adaptive IIR filters, System identification, Particle Swarm Optimization

REFERENCES:

[1] M. Nishimura, J. Yoshimoto, Y. Tokita, Y. Nkamura, and S. Ishii, ”Control of Real Acrobot by Learning the Switching Rule of Multiple Controllers,” The IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences (Japanese Edition), Vol. J88-A, No. 5 pp. 646-657, 2005.

[2] D. Merkle and M. Middendorf, ”Swarm intelligence and signal processing,” IEEE Signal Processing Mag., Vol. 25, No. 6, pp. 152-158, 2008.

[3] K. Su and H. P. Cai, ”A modified SQP-filter method for nonlinear complementarily problem,” Appl. Math. Model. Vol. 33, No. 6, pp. 2890-2896, 2009.

[4] L. Lin and C. Chen, ”Parameter estimation of chaotic systems by an oppositional seeker optimization algorithm,” Nonlinear Dyn., Vol. 76, No. 1, pp.509-517, 2014.

[5] Shanhe Jiang, Yan Wang, Zhicheng JiA, ”new design method for adaptive IIR system identifi- cation using hybrid particle swarm optimization and gravitational search algorithm,” Nonlinear Dynamics, Vol. 79, No. 4, pp. 2553?2576, 2015.

[6] E. Cuevas, J. Galvez, S. Hinojosa, O. Avalos, ´ D. Zald´ıvar, M. Perez-Cisneros, ”A Comparison ´ of Evolutionary Computation Techniques for IIR Model Identification,” Journal of Applied Mathematics, Vol. 2014, Article ID : 827206, 2014.

[7] A. Hartmanna, J. M. Lemosa, R. S. Costaa, and S. Vinga, ”Identifying IIR Filter Coefficients using Particle Swarm Optimization with Application to Reconstruction of Missing Cardiovascular Signals,” Engineering Applications of Artifi- cial Intelligence, Vol. 34, pp. 193-198, 2014.

[8] E. Martinez-Ayala, V. Ayala-Ramirez, and R. E. Sanchez-Yanez, ”Noisy Signal Parameter Identification using Particle Swarm Optimization,” CONIELECOMP, 2011, Electronics, Communications, and Computers, International Conference on, Electronics, Communications, and

[7] A. Hartmanna, J. M. Lemosa, R. S. Costaa, and S. Vinga, ”Identifying IIR Filter Coefficients using Particle Swarm Optimization with Application to Reconstruction of Missing Cardiovascular Signals,” Engineering Applications of Artifi- cial Intelligence, Vol. 34, pp. 193-198, 2014.

[8] E. Martinez-Ayala, V. Ayala-Ramirez, and R. E. Sanchez-Yanez, ”Noisy Signal Parameter Identification using Particle Swarm Optimization,” CONIELECOMP, 2011, Electronics, Communications, and Computers, International Conference on, Electronics, Communications, and Computers, International Conference on 2011, pp. 142-146, 2011.

[9] L. Gupta and R. Mehra, ”Modified PSO based Adaptive IIR Filter Design for System Identifi- cation on FPGA,” International Journal of Computer Applications, Vol. 22, No. 5 pp. 1-7, 2011.

[10] N. Karaboga, ”Digital IIR Filter Design using Differential Evolution Algorithm, ” EURASIP Journal on Applied Signal Processing, Vol. 2005, pp. 1269-1276, 2005.

[11] J. Kennedy and R. Eberhart, ”Particle Swarm Optimization,” Proc. IEEE international conference on neural networks, Vol. 4, pp. 1942-1948, 1995.

[12] D. E. Goldberg, ”Genetic Algorithms in Search, Optimization and Machine Learning,” AddisonWesley Professional, pp.1-25, 1989.

[13] R. Hassan, B. Cohanim, O. de Weck, and G. Venter, ”A Comparison of Particle Swarm Optimization and The Genetic Algorithm,” 1st AIAA multidisciplinary design optimization specialist conference, No. AIAA-2005-1897, 2005.

[14] N. K. Rout, D. P. Das, and G. Panda, ”Particle Swarm Optimization based Active Noise Control Algorithm without Secondary Path Identi- fication,” IEEE Transactions on Instrumentation and Measurement, Vol. 61, No. 2, pp. 554-563, 2012.

[15] R. Mendes, J. Kennedy, and J. Neves, ”The Fully Informed Particle Swarm: Simpler, Maybe Better,” IEEE Transactions on Evolutionary Computation, Vol. 8, No. 3, pp.204-210, 2004.

[16] G. I. Evers and M. B. Ghalia, ”Regrouping Particle Swarm Optimization: A New Global Optimization Algorithm with Improved Performance Consistency Across Benchmarks,” Proc. of IEEE International conference on Systems, Man, and Cybernetics, pp. 3901-3908, 2009.

[17] P. N. Suganthan, ”Particle swarm optimisation with a neighbourhood operator,” Proc. of Congress on Evolutionary Computation, vol. 3, pp. 1958-1962, IEEE Press, Piscataway, 1999.

[18] M. S. White and S. J. Flockton, ”Adaptive Recursive Filtering using Evolutionary Algorithms,” Evolutionary Algorithms in Engineering Applications, pp. 361-376, 1997.