Training of the dynamic neural networks via contstrained optimisation

IEEE International Conference on Neural Networks - Conference Proceedings

Volumn 1, Issue , 2004, Pages 195-200

  Patan, Krzysztof ^a  

^a UNIVERSITY OF ZIELONA GÓRA   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; CONSTRAINT THEORY; FEEDBACK; IDENTIFICATION (CONTROL SYSTEMS); LEARNING SYSTEMS; OPTIMIZATION; VECTORS;

BOUNDED INPUT BOUNDED OUTPUT (BIBO); INFINITE IMPULSE RESPONSE (IIR) FILTERS; LOCALLY RECURRENT GLOBALLY FEED-FORWARD (LRGF) NETWORKS; NEURON MODELS;

NEURAL NETWORKS;

EID: 10944222020     PISSN: 10987576     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. M. Ayoubi, "Fault diagnosis with dynamic neural structure and application to a turbo-charger". Proceeding of Int. Symp. Fault Detection Supervision and Safety for Technical Processes, SAFEPROCESS'94, Espoo, Finland, 1994, pp.618-623.
2. J. M. F. Calado, J. Korbicz, K. Patan, R.J. Patton, J. M. G. Sa da Costa, "Soft computing approaches to fault diagnosis for dynamic systems", European Journal of Control, vol. 7, pp.248-286, 2001.
3. M. M. Gupta, D. H. Rao, "Dynamic neural units with application to the control of unknown nonlinear systems", Journal of Intelligent and Fuzzy Systems, vol. 1, pp.73-92, 1993.
4. M. M. Gupta, L. Jin, N. Homma, Static and Dynamic Neural Neetworks. From Fundamentals to Advanced Theory, New Jersey: John Wiley & Sons, 2003.
5. Y. Fang, T. G. Kincaid, "Stability analysis of dynamical neural networks", IEEE Transactions on Neural Networks, vol. 7, pp.996-1006, 1996.
6. C. Feng, R. Plamondon, "On the stability analysis of delayed neural networks system", Neural Networks, vol. 14, pp.1181-1188, 2001.
7. O. Nelles, Nonlinear System Identification. From Classical Approaches to Neural Networks and Fuzzy Models, Berlin: Springer-Verlag, 2001.
8. K. Patan, "Artificial dynamic neural networks and their application in modelling of industrial processes", Ph.D. dissertation, Warsaw University of Technology, Faculty of Mechatronics, Warsaw, 2000.
9. K. Patan, T. Parisini, "Stochastic learning methods for dynamic neural networks: simulated and real-data comparisons", Proceedings of the 2002 American Control Conference, ACC'02, Anchorage, Alaska, USA, 2002, pp.2577-2582.
10. K. Patan, J. Korbicz, Artificial neural networks in fault diagnosis, in Fault Diagnosis. Models, Artificial Intelligence, Applications, J. Korbicz, J. M. Koscielny, Z. Kowalczuk, W. Cholewa (Eds.), Berlin:Springer-Verlag, pp.333-380, 2004.
11. J. C. Spall, Stochastic Optimization, Stochastic Approximation and Simulated Annealing, in Encyclopedia of Electrical and Electronics Engineering, J. G. Webster (Ed.), New York: Wiley, 1999.
12. J. C. Spall, Introduction to Stochastic Search and Optimization, New Jersey: John Willey & Sons, 2003.
13. A. Ch. Tsoi, A. D. Back, "Locally recurrent globally feedforward networks: A critical review of architectures", vol. 5, pp.229-239, 1994.
14. E. Walter, L. Pronzato, Identification of Parametric Models from Experimental Data, London: Springer-Verlag, 1996.
15. J. M. Zamarreno, P. Vega, "State space neural network. Properties and application", Neural Networks, vol. 11, pp.1099-1112, 1998.