Stochastic approaches to dynamic neural network training. Actuator fault diagnosis study

IFAC Proceedings Volumes (IFAC-PapersOnline)

Volumn 15, Issue 1, 2002, Pages 53-58

  Patan, Krzysztof ^a   Parisini, Thomas ^b  

^a UNIVERSITY OF ZIELONA GÓRA   (Poland)

^b UNIVERSITY OF TRIESTE   (Italy)

Author keywords

Actuators; Dynamic modelling; Fault detection; Learning algorithms; Neural network models

Indexed keywords

ACTUATORS; AUTOMATION; BACKPROPAGATION ALGORITHMS; DYNAMIC MODELS; FAILURE ANALYSIS; LEARNING ALGORITHMS; NEURAL NETWORKS; STOCHASTIC SYSTEMS; SUGAR FACTORIES;

CLASSICAL BACK-PROPAGATION; DYNAMIC NEURAL NETWORKS; FAULT DIAGNOSIS SYSTEMS; NEURAL NETWORK MODEL; STOCHASTIC ALGORITHMS; STOCHASTIC APPROACH; STOCHASTIC METHODS; TRAINING ALGORITHMS;

FAULT DETECTION;

EID: 84945587147     PISSN: 14746670     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3182/20020721-6-es-1901.00740     Document Type: Conference Paper

References (14)

1. Alessandri, A. and T. Parisini (1997). Nonlinear modelling of complex large-scale plants using neural networks and stochastric approximation. IEEE Trans. Systems, Man, and Cybernetics - A (27), 750-757.
2. Ayoubi, M. (1994). Fault diagnosis with dynamic neural structure and application to a turbo-charger. In: Proc. Int. Symp. Fault Detection Supervision and Safety for Technical Processes, SAFEPROCESS'94, Espoo, Finland. Vol. 2. pp. 618-623.
3. Chen, J. and R. J. Patton (1999). Robust Model-Based Fault Diagnosis for Dynamic Systems. Kluwer Academic Publishers. Berlin.
4. Frank, P. M. and B. Köppen-Seliger (1997). New developments using AI in fault diagnosis. Artificial Intelligence 10(1), 3-14.
5. Korbicz, J., K. Patan and A. Obuchowicz (1999). Dynamic neural networks for process modelling in fault detection and isolation systems. International Journal of Applied Mathematics and Computer Science 9(3), 519-546.
6. Korbicz, J., K. Patan and A. Obuchowicz (2001). Neural network fault detection system for dynamic processes. Bulletin of the Polish Academy of Sciences. Technical Sciences. 49(2), 301-321.
7. Lissane Elhaq, S., F. Giri and H. Unbehauen (1999). Modelling, identification and control of sugar evaporation - theoretical design and experimental evaluation. Control Engineering Practice (7), 931-942.
8. Patan, K. (2000). Artificial Dynamic Neural Networks and Their Application in Modelling of Industrial Processes. Ph.D. dissertation. Warsaw University of Technology, Faculty of Mechatronics. Warszawa.
9. Patan, K. and J. Korbicz (2000). Application of Dynamic Neural Networks in an Industrial Plant. In: Proc. Int. Symp. Fault Detection Supervision and Safety for Technical Processes, SAFEPROCESS'2000, Budapest, Hungary. pp. 186-191.
10. Patton, R. J., P. M. Frank and R.N. Clark (2000). Issues of Fault Diagnosis for Dynamic Systems. Springer-Verlag. Berlin.
11. Spall, J.C. (1992). Multivariate stochastic aproximation using a simultaneous perturbation gradient approximation. IEEE Trans. Automatic Control (37), 332-341.
12. Spall, J.C. (1999). Stochastic optimization, stochastic approximation and simulated annealing. In: Encyclopedia of Electrical and Electronics Engineering (J.G. Webster, (Ed.). John Wiley & Sons. New York.
13. Walter, E. and L. Pronzato (1996). Identification of Parametric Models from Experimental Data. Springer. London.
14. Żurada, J. M. (1993). Lambda learning rule for feedforward neural networks. In: Proc. Int. Conf. on Neural Networks. San Francisco, USA, March 28 - April 1. pp. 1808-1811.