A STATE-SPACE NEURAL NETWORK FOR MODELING DYNAMICAL NONLINEAR SYSTEMS

Karima Amoura, Patrice Wira, Said Djennoune

Abstract

In this paper, a specific neural-based model for identification of dynamical nonlinear systems is proposed. This artificial neural network, called State-Space Neural Network (SSNN), is different from other existing neural networks. Indeed, it uses a state-space representation while being able to adapt and learn its parameters. These parameters are the neural weights which are intelligible or understandable. After learning, the SSNN therefore is able to provide a state-space model of the dynamical nonlinear system. Examples are presented which show the capability of the SSNN for identification of multivariate dynamical nonlinear systems.

References

  1. Chen, F. and Khalil, H. (1992). Adaptive control of nonlinear systems using neural networks. International Journal of Control, 55(6):1299-1317.
  2. Elman, J. (1990). Finding structure in time. Cognitive Science, 14(2):179-211.
  3. Gauthier, J.-P. and Kupka, I. (2001). Deterministic observation theory and applications. Cambridge University Press, Cambridge, UK.
  4. Gonzalez, P. A. and Zamarreno, J. M. (2002). A short-term temperature forecaster based on a state space neural network. Engineering Applications of Artificial Intelligence, 15(5):459-464.
  5. Haykin, S. (1994). Neural Networks : A comprehensive Foundation. Macmillan College Publishing Company, Inc., New York.
  6. Kim, Y. H., Lewis, F. L., and Abdallah, C. T. (1997). A dynamic recurrent neural-network-based adaptive observer for a class of nonlinear systems. Automatica, 33(8):1539-1543.
  7. Mirikitani, D. and Nikolaev, N. (2010). Recursive bayesian recurrent neural networks for time-series modeling. IEEE Transactions on Neural Networks, 21(1):262 - 274.
  8. Narendra, K. and Parthasarathy, K. (1990). Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks, 1(1):4-27.
  9. Principe, J. C., Euliano, N. R., and Lefebvre, W. C. (2000). Neural and Adaptive Systems: Fundamentals Through Simulations. John Wiley and Sons.
  10. Werbos, P. (1974). Beyond Regression: New tools for prediction and analysis in the behavioral sciences. Ph.d. thesis, Harvard University.
  11. Zamarreno, J. and Vega, P. (1998). State space neural network. properties and application. Neural Networks, 11(6):1099-1112.
  12. Zamarreno, J., Vega, P., Garca, L., and Francisco, M. (2000). State-space neural network for modelling, prediction and control. Control Engineering Practice, 8(9):1063-1075.
  13. Zamarreno, J. M. and Vega, P. (1999). Neural predictive control. application to a highly non-linear system. Engineering Applications of Artificial Intelligence, 12(2):149-158.
Download


Paper Citation


in Harvard Style

Amoura K., Wira P. and Djennoune S. (2011). A STATE-SPACE NEURAL NETWORK FOR MODELING DYNAMICAL NONLINEAR SYSTEMS . In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011) ISBN 978-989-8425-84-3, pages 369-376. DOI: 10.5220/0003680503690376


in Bibtex Style

@conference{ncta11,
author={Karima Amoura and Patrice Wira and Said Djennoune},
title={A STATE-SPACE NEURAL NETWORK FOR MODELING DYNAMICAL NONLINEAR SYSTEMS},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)},
year={2011},
pages={369-376},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003680503690376},
isbn={978-989-8425-84-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)
TI - A STATE-SPACE NEURAL NETWORK FOR MODELING DYNAMICAL NONLINEAR SYSTEMS
SN - 978-989-8425-84-3
AU - Amoura K.
AU - Wira P.
AU - Djennoune S.
PY - 2011
SP - 369
EP - 376
DO - 10.5220/0003680503690376