NONLINEAR SYSTEM IDENTIFICATION USING DISCRETE-TIME NEURAL NETWORKS WITH STABLE LEARNING ALGORITHM
Talel Korkobi, Mohamed Djemel, Mohamed Chtourou
2008
Abstract
This paper presents a stable neural sytem identification for nonlinear systems. An input output discrete time representation is considered. No a priori knowledge about the nonlinearities of the system is assumed. The proposed learning rule is the backpropagation algorithm under the condition that the learning rate belongs to a specified range defining the stability domain. Satisfying such condition, unstable phenomenon during the learning process is avoided. A Lyapunov analysis is made in order to extract the new updating formulation which contain a set of inequality constraints. In the constrained learning rate algorithm, the learning rate is updated at each iteration by an equation derived using the stability conditions. As a case study, identification of two discrete time systems is considered to demonstrate the effectiveness of the proposed algorithm. Simulation results concerning the considered systems are presented.
References
- B. Egardt, 1979. Stability of adaptive controllers. in: Lecture Notes in Control and Information Sciences 20, Springer-Verlag, Berlin.
- Z. Feng, A.N. Michel, 1999. Robustness analysis of a class of discrete-time systems with applications to neural networks, in: Proceedings of American Control Conference, San Deigo, pp. 3479-3483.
- S.S. Ge, C.C. Hang, T.H. Lee, T. Zhang, 2001: Stable Adaptive Neural Network Control, Kluwer Academic, Boston.
- P. A. Ioannou, J. Sun, 2004. Robust Adaptive Control, Information Sciences 158, 31-147, Prentice-Hall, Upper Saddle River.
- L. Jin, M.M. Gupta, 1999. Stable dynamic backpropagation learning in recurrent neural networks, IEEE Trans. Neural Networks 10 (6) , 1321-1334.
- Z. P. Jiang, Y. Wang, 2001. Input-to-state stability for discrete-time nonlinear systems, Automatica 37 (2) , 857-869.
- E. B. Kosmatopoulos, M.M. Polycarpou, M.A. Christodoulou, P.A. Ioannou, 1995. High-order neural network structures for identification of dynamical systems, IEEE Trans. Neural Networks 6 (2), 431-442.
- M. M. Polycarpou, P.A. Ioannou 1992. Learning and convergence analysis of neural-type structured networks, IEEE Trans. Neural Networks 3 (1) ,39- 50.
- J. A. K. Suykens, J. Vandewalle, B. De Moor, 1997. NLq theory: checking and imposing stability of recurrent neural networks for nonlinear modelling, IEEE Trans. Signal Process (special issue on neural networks for signal processing) 45 (11) , 2682-2691.
- W. Yu, X. Li, 2001. Some stability properties of dynamic neural networks, IEEE Trans. Circuits Syst., Part I 48 (1) , 256-259.
- W. Yu, A.S. Poznyak, X. Li, 2001. Multilayer dynamic neural networks for nonlinear system on-line identification, Int. J. Control 74 (18), 1858-1864.
- E. Barn, 1992. Optimisation for training neural nets, IEEE Trans. Neural Networks 3 (2) , 232-240.
- Ching-Hang Lee and al, 2002. Control of Nonlinear Dynamic Systems Via adaptive PID Control Scheme with Daturation Bound, International Journal of Fuzzy Systems, Vol. 4 No. 4 ,922-927.
- K. S. Narendra and K. Parthasarathy, 1990. Identification and control of dynamical systems using neural networks, IEEE Transaction Neural Networks, vol.1, pp.4-27, Mar.
- J. D. Boskovic and K.S.Narendra 1995. Comparison of linear nonlinear and neural-network based adaptive controllers for a class of fed-batch fermentation process, Automatica, vol. 31, no. 6, pp. 537-547.
Paper Citation
in Harvard Style
Korkobi T., Djemel M. and Chtourou M. (2008). NONLINEAR SYSTEM IDENTIFICATION USING DISCRETE-TIME NEURAL NETWORKS WITH STABLE LEARNING ALGORITHM . In Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-8111-30-2, pages 351-356. DOI: 10.5220/0001504403510356
in Bibtex Style
@conference{icinco08,
author={Talel Korkobi and Mohamed Djemel and Mohamed Chtourou},
title={NONLINEAR SYSTEM IDENTIFICATION USING DISCRETE-TIME NEURAL NETWORKS WITH STABLE LEARNING ALGORITHM},
booktitle={Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2008},
pages={351-356},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001504403510356},
isbn={978-989-8111-30-2},
}
in EndNote Style
TY - CONF
JO - Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - NONLINEAR SYSTEM IDENTIFICATION USING DISCRETE-TIME NEURAL NETWORKS WITH STABLE LEARNING ALGORITHM
SN - 978-989-8111-30-2
AU - Korkobi T.
AU - Djemel M.
AU - Chtourou M.
PY - 2008
SP - 351
EP - 356
DO - 10.5220/0001504403510356