Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31103
Hybrid Algorithm for Hammerstein System Identification Using Genetic Algorithm and Particle Swarm Optimization

Authors: Tomohiro Hachino, Kenji Shimoda, Hitoshi Takata


This paper presents a method of model selection and identification of Hammerstein systems by hybridization of the genetic algorithm (GA) and particle swarm optimization (PSO). An unknown nonlinear static part to be estimated is approximately represented by an automatic choosing function (ACF) model. The weighting parameters of the ACF and the system parameters of the linear dynamic part are estimated by the linear least-squares method. On the other hand, the adjusting parameters of the ACF model structure are properly selected by the hybrid algorithm of the GA and PSO, where the Akaike information criterion is utilized as the evaluation value function. Simulation results are shown to demonstrate the effectiveness of the proposed hybrid algorithm.

Keywords: Identification, Genetic Algorithm, Particle Swarm Optimization, Hammerstein System, automatic choosing function model

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1224


[1] T. Liu, S. Boumaiza and F. M. Ghannouchi, Augmented Hammerstein predistorter for linearization of broad-band wireless transmitters, IEEE Trans. Microwave Theory and Techniques, Vol. 54, No.4, pp. 1340-1349, 2006.
[2] F. Alonge, F. D-Ippolito, F. M. Raimondi and S. Tumminaro, Nonlinear modeling of dc/dc converters using the Hammerstein-s approach, IEEE Trans. Power Electronics, Vol. 22, No. 4, pp. 1210-1221, 2007.
[3] O. Nelles, Nonlinear System Identification, Springer, 2000.
[4] S. A. Billings and S. Y. Fakhouri, Identification of systems containing linear dynamic and static nonlinear elements, Automatica, Vol. 18, No. 1, pp. 15-26, 1982.
[5] H. Al-Duwaish and M. N. Karim, A new method for the identification of Hammerstein model, Automatica, Vol. 33, No. 10, pp. 1871-1875, 1997.
[6] F. C. Kung and D. H. Shih, Analysis and identification of Hammerstein model non-linear delay systems using block-pulse function expansions, Int. J. Control, Vol. 43, No. 1, pp. 139-147, 1986.
[7] S. Adachi and H. Murakami, Generalized predictive control system design based on non-linear identification by using Hammerstein model (in Japanese), Trans. ISCIE, Vol. 8, No. 3, pp. 115-121, 1995.
[8] T. Hatanaka, K. Uosaki and M. Koga, Evolutionary computation approach to Hammerstein model identification, Proc. 4th Asian Control Conf., pp. 1730-1735, 2002.
[9] T. Hachino and H. Takata, Structure selection and identification of Hammerstein type nonlinear systems using automatic choosing function model and genetic algorithm, IEICE Trans. Fundamentals of Electronics, Communications and Computer Sciences, Vol. E88-A, No. 10, pp.2541- 2547, 2005.
[10] D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, 1989.
[11] J. Kennedy and R. C. Eberhart, Particle swarm optimization, Proc. IEEE Int. Conf. Neural Networks, pp. 1942-1948, 1995.
[12] Y. Shi and R. C. Eberhart, Empirical study of particle swarm optimization, Proc. 1999 Congress on Evolutionary Computation, pp. 1945-1950, 1999.
[13] H. Yoshida, K. Kawata, Y. Fukuyama, S. Takayama and Y. Nakanishi, A particle swarm optimization for reactive power and voltage control considering voltage security assessment, IEEE Trans. Power Syst., Vol. 15, No. 4, pp. 1232-1239, 2000.
[14] A. Ide and K. Yasuda, A basic study of the adaptive particle swarm optimization (in Japanese), IEEJ Trans. EIS, Vol. 124, No. 2, pp. 550- 557, 2004.
[15] M. Clerc and J. Kennedy, The particle swarm - explosion, stability, and convergence in a multidimensional complex space, IEEE Trans. Evolutionary Computation, Vol. 6, No. 1, pp. 58-73, 2002.
[16] V. Kadirkamanathan, K. Selvarajah and P. J. Fleming, Stability analysis of the particle dynamics in particle swarm optimizer, IEEE Trans. Evolutionary Computation, Vol. 10, No. 3, pp. 245-255, 2006.
[17] H. Akaike, A new look at the statistical model identification, IEEE Trans. Automatic Control, Vol. 19, No. 6, pp. 716-723, 1974.
[18] H. Takata, An automatic choosing control for nonlinear systems, Proc. of the 35th IEEE CDC, pp.3453-3458, 1996.