Non linear process identification using a neural network based multiple models generator

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2687, Issue , 2003, Pages 647-654

  Madani, Kurosh ^a   Rybnik, Mariusz ^a   Chebira, Abdennasser ^a  

^a UNIVERSITÉ PARIS EST CRÉTEIL   (France)

Author keywords

[No Author keywords available]

Indexed keywords

DATA HANDLING; FAULT DETECTION; LINEAR SYSTEMS; NEURAL NETWORKS; NONLINEAR SYSTEMS; DATA PROCESSING; FAILURE ANALYSIS; LINEARIZATION;

DATA DRIVEN; LINEAR PROCESS; MODEL BASED CONTROLS; MODEL COMPLEXITY; MULTIPLE-MODELING; NONLINEAR PROCESS; REAL-WORLD; ARTIFICIAL NEURAL NETWORK; FAULT DIAGNOSIS; MODELING COMPLEXITY; MULTIPLE MODELS; NON LINEAR SYSTEM; NON-LINEARITY; REAL-WORLD APPLICATION; SYSTEM DESIGN;

COMPLEX NETWORKS; IDENTIFICATION (CONTROL SYSTEMS);

EID: 25144473310     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44869-1_82     Document Type: Article

References (15)

1. Wiener N., Non linear Problems in Random Theory, The Technology Press MIT, and John Wiley, New York, 1958.
2. Schetzen M., The Voltera and Wiener Theories of Nonlinear Systems John Wiley, New York, 1980.
3. Zadeh L., Outline of a New Approach to the Analysis of Complex Systems and Decision Processes, IEEE Trans. On Systems, Man and Cybernetics 3, pp. 28-44.
4. Takagi T. and Sugeno M., Fuzzy identification of systems and its application to modeling and control. IEEE Trans. on Systems Man and Cyberneticc, Vol. 15, 1985, pp. 116-132.
5. Narendra, K.S. and Parthasarathy K., Identification and control of dynamical systems using neural networks, Vol. 1, No. 1, 1990.
6. Nelles O., On the identification with neural networks as series-parallel and parallel models, International Conference on Artificial Neural Networks (ICANN'95), Paris, France, 1995.
7. Nelles O., Orthonormal basis functions for nonlinear system identification with local linear model trees (LOLIMOT). Proc of SYSID'97, Fukuoka, Japan. Vol.2, 1997, pp. 667-672.
8. Mayoubi M., Schafer M., Sinsel S., "Dynamic Neural Units for Non-linear Dynamic Systems Identification", LNCS Vol. 930, Springer Verlag, 1995, pp.1045-1051.
9. Faller W., Schreck S., Real-Time Prediction Of Unsteady Aerodynamics : Application for Aircraft Control and Maneuverability Enhancement, IEEE Transactions on Neural Networks, Vol. 6, Nj 6, Nov. 1995.
10. Goonatilake S. and Khebbal S.: Issues, Classification and Future Directions. In Intelligent Hybrid Systems. John Wiley & Sons, 1995, pp 1-20, ISBN 0 471 94242 1.
11. Murray-Smith R. and Johansen T.A., Multiple Model Approaches to Modeling and Control, edited by R. Murray-Smith and T.A. Johansen, Taylor & Francis Publishers, 1997, ISBN 0-7484-0595-X.
12. Sridhar D.V., Bartlett E.B., Seagrave R.C., "An information theoretic approach for combining neural network process models", Neural Networks, Vol. 12, pp 915-926, Pergamon, Elsevier, 1999.
13. th IEEE Conf. on Decision and Control, Tampa, Florida, USA.
14. Boukhris, A, Mourot G. and Ragot J. (2000). Nonlinear dynamic system identification: a multiple-model approach. Int. J. of control, Vol. 72, No7/8, pp. 591-604.
15. Kohonen T., "Self-Organization and Associative Memory", Springer-Verlag, 1984.