Hybrid Algorithm for Hammerstein System Identification Using Genetic Algorithm and Particle Swarm Optimization
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.
[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.
[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.
@article{"International Journal of Electrical, Electronic and Communication Sciences:53099", author = "Tomohiro Hachino and Kenji Shimoda and Hitoshi Takata", title = "Hybrid Algorithm for Hammerstein System Identification Using Genetic Algorithm and Particle Swarm Optimization", abstract = "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 = "Hammerstein system, identification, automatic choosing
function model, genetic algorithm, particle swarm optimization.", volume = "3", number = "5", pages = "1127-6", }
