NEURAL NETWORK BASED HAMMERSTEIN SYSTEM IDENTIFICATION USING PARTICLE SWARM SUBSPACE ALGORITHM

S. Z. Rizvi, H. N. Al-Duwaish

Abstract

This paper presents a new method for modeling of Hammerstein systems. The developed identification method uses state-space model in cascade with radial basis function (RBF) neural network. A recursive algorithm is developed for estimating neural network synaptic weights and parameters of the state-space model. No assumption on the structure of nonlinearity ismade. The proposed algorithm works under the weak assumption of richness of inputs. The problem of modeling is solved as an optimization problem and Particle Swarm Optimization (PSO) is used for neural network training. Performance of the algorithm is evaluated in the presence of noisy data and Monte-Carlo simulations are performed to ensure reliability and repeatability of the identification technique.

References

  1. Abonyi, I., Nagy, L., Szeifert, E. (2000). Hybrid fuzzy convolution modelling and identification of chemical process systems. In International Journal Systems Science. volume 31, pages 457-466.
  2. Al-Duwaish, H. (2001). A genetic approach to the identification of linear dynamical systems with static nonlinearities. In International Journal of Systems Science. volume 31, pages 307-314.
  3. Al-Duwaish, H., Nazmulkarim, M., Chandrasekar, V. (1997). Hammerstein model identification by multilayer feedforward neural networks. In International Journal Systems Science. volume 28, pages 49-54.
  4. Billings, S. (1980). Identification of nonlinear systems - a survey. In IEE Proceedings. volume 127, pages 272- 285.
  5. Carlisle, A., Dozier, G. (2001). An off-the-shelf pso. In In Proceedings of the Particle Swarm Optimization Workshop. pages 1-6.
  6. Eberhart, R., Shi, Y. (1998). Parameter selection in particle swarm optimisation. In Evolutionary Programming VII. pages 591-600.
  7. Eskinat, E., Johnson, S. H., Luyben, W. L. (1991). Use of hammerstein models in identification of nonlinear systems. In AIChE Journal. volume 37, pages 255- 268.
  8. Fruzzetti, K. P., Palazoglu A., McDonald, K. A. (1997). Nonlinear model predictive control using hammerstein models. In J. Proc. Control. vol. 7, page 31-41.
  9. Goethals, I., Pelckmans, K., Suykens, J. A. K., Moor, B. D. (2005). Identification of mimo hammerstein models using least squares support vector machines. In Automatica. volume 41, pages 1263-1272.
  10. Greblicki, W. (1989). Non-parametric orthogonal series identification of hammerstein systems. In International Journal Systems Science. volume 20, pages 2335-2367.
  11. Haddad, A. H., Thomas, J. B. (1968). On optimal and suboptimal nonlinear filters for discrete inputs. In lEEE Transaction on Information Theory. volume 14, pages 16-21.
  12. Haykin, S. (1999). Neural Networks - A Comprehensive Foundation. Prentice-Hall, Second Edition.
  13. Katayama, T. (2005). Subspace Methods for System Identification. Springer-Verlag, London.
  14. Kennedy, J., Eberhart, R. (2001). Swarm Intelligence. Academic Press.
  15. Luo, D., Leonessa, A. (2002). Identification of mimo hammerstein systems with nonlinear feedback. In Proceedings of American Control Conference, Galesburg, USA. pages 3666-3671.
  16. Narendra, K. S., Gallman, P. (1966). An iterative method for the identification of nonlinear systems using hammerstein model. In IEEE Transaction on Automatic Control. volume 11, pages 546-550.
  17. Overschee, P. V., Moor, B. D. (1994). N4sid: Subspace algorithms for the identification of combined deterministic-stochastic systems. In Automatica. volume 30, pages 75-93.
  18. Vörös, J. (2002). Modeling and paramter identification of systems with multisegment piecewise-linear characteristics. In IEEE Transaction on Automatic Control. volume 47, pages 184-188.
  19. Verhaegen, M., Westwick, D. (1996). Identifying mimo hammerstein systems in the context of subspace model identification methods. In International Journal Control. volume 63, pages 331-349.
  20. Wenxiao, Z. (2007). Identification for hammerstein systems using extended least squares algorithm. In Proceedings of 26th Chinese Control Conference, China,. pages 241-245.
  21. Zhao, W., Chen, H. (2006). Recursive identification for hammerstein systems with arx subsystem. In IEEE Transaction on Automatic Control. volume 51, pages 1966-1974.
Download


Paper Citation


in Harvard Style

Z. Rizvi S. and N. Al-Duwaish H. (2010). NEURAL NETWORK BASED HAMMERSTEIN SYSTEM IDENTIFICATION USING PARTICLE SWARM SUBSPACE ALGORITHM . In Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010) ISBN 978-989-8425-32-4, pages 182-189. DOI: 10.5220/0003072401820189


in Bibtex Style

@conference{icnc10,
author={S. Z. Rizvi and H. N. Al-Duwaish},
title={NEURAL NETWORK BASED HAMMERSTEIN SYSTEM IDENTIFICATION USING PARTICLE SWARM SUBSPACE ALGORITHM},
booktitle={Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010)},
year={2010},
pages={182-189},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003072401820189},
isbn={978-989-8425-32-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010)
TI - NEURAL NETWORK BASED HAMMERSTEIN SYSTEM IDENTIFICATION USING PARTICLE SWARM SUBSPACE ALGORITHM
SN - 978-989-8425-32-4
AU - Z. Rizvi S.
AU - N. Al-Duwaish H.
PY - 2010
SP - 182
EP - 189
DO - 10.5220/0003072401820189