Confidence estimation of GMDH neural networks and its application in fault detection systems

International Journal of Systems Science

Volumn 39, Issue 8, 2008, Pages 783-800

  Korbicz, Jozef ^a   Mrugalski, Marcin ^a  

^a UNIVERSITY OF ZIELONA GÓRA   (Poland)

Author keywords

Application; Bounded error modelling; Fault diagnosis; Model uncertainty; Neural networks; Non linear systems; System identification

Indexed keywords

ARTIFICIAL INTELLIGENCE; BENCHMARKING; DATA HANDLING; ELECTRIC FAULT LOCATION; ERROR ANALYSIS; ESTIMATION; FAULT DETECTION; IMAGE CLASSIFICATION; PARAMETER ESTIMATION; SENSORS; SIGNAL DETECTION; UNCERTAINTY ANALYSIS; VEGETATION;

(001) PARAMETER; CONFIDENCE ESTIMATION; FAULT DETECTION SYSTEMS; GROUP METHOD OF DATA HANDLING (GMDH); MATHEMATICAL DESCRIPTIONS; MODEL OUTPUTS; MODEL UNCERTAINTIES; NETWORK STRUCTURES; ROBUST FAULT DETECTION; SYSTEM IDENTIFICATION (SI);

NEURAL NETWORKS;

EID: 46149124817     PISSN: 00207721     EISSN: 14645319     Source Type: Journal    
DOI: 10.1080/00207720701847745     Document Type: Article

References (40)

1. Atkinson, A.C., and Donev, A.N. (1992), Optimum Experimental Designs, New York: Oxford University Press. Back, A.D., and Tsoi, A.C. (1991), "FIR and IIR synapses. A new neural network architectures for time series modelling," Neural Computation, 3, 375-385.
2. Bartyś, M., Patton, R.J., Syfert, M., Heras, S., and Quevedo, J. (2006), "Introduction to the DAMADICS actuator FDI benchmark study," Control Engineering Practice, 14, 577-596.
3. Blanke, M., Kinnaert, M., Lunze, J., and Staroswiecki, M. (2003) (eds.), Diagnosis and Fault-Tolerant Control, New York: Springer-Verlag.
4. Bouthiba, T. (2004), "Fault location in ehv transmission lines using artificial neural networks," International Journal of Applied Mathematics and Computer Science, 14, 69-78.
5. Calado, J.M.F., Korbicz, J., Patan, K., Patton, R.J., and Sa da Costa, J.M.G (2001), "Soft computing approaches to fault diagnosis for dynamic systems," European Journal of Control, 7, 248-286.
6. Chen, J., and Patton, R.J. (1999), International Journal of Applied Mathematics and Computer Science, Berlin: Kluwer Academic Publishers.
7. Delaleau, E., Louis, J.P., and Ortega, R. (2001), "Modeling and control of induction motors," International Journal of Applied Mathematics and Computer Science, 11, 105-129.
8. Etien, E., Cauet, S., Rambault, L., and Champenois, G. (2001), "Control of an Induction Motor Using Sliding Mode Linearization," International Journal of Applied Mathematics and Computer Science, 12, 523-531.
9. Farlow, S.J. (1984), Self-organizing Mehods in Modelling - GMDH Type Algorithms, New York: Marcel Dekker.
10. Fasconi, P., Gori, M., and Soda, G. (1992), "Local feedback multilayered networks," Neural Computation, 4, 120-130.
11. Gori, M., Bengio, Y., and Mori, R.D. (1989), "BPS: A learning algorithm for capturing the dynamic nature of speech," Neural Computation, 2, 417-423.
12. Gupta, M.M., Liang, J., and Homma, N. (2003), Static and Dynamic Neural Networks, Hoboken: New Jersey: John Wiley & Sons.
13. Herskovits, J., Mappa, P., Goulart, E., and Mota Soares, C.M. (2005), "Mathematical programming models and algorithms for engineering design optimization," Computer Methods in Applied Mechanics and Engeering, 194, 3244-3268.
14. Isermann, R. (2005), Fault-Diagnosis Systems. An Introduction from Fault Detection to Fault Tolerance, Berlin: Springer.
15. Ivakhnenko, A.G., and Mueller, J.A. (1995), "Self-organizing of nets of active neurons," System Analysis Modelling Simulation, 20, 93-106.
16. Korbicz, J., Kościelny, J.M., Kowalczuk, Z., and Cholewa, W. (2004) (eds.), Fault Diagnosis. Models, Artificial Intelligence, Applications, Berlin: Springer-Verlag.
17. Milanese, M., Norton, J., Piet-Lahanier, H., and Walter, E. (1996) (eds.), Bounding Approaches to System Identification, New York: Plenum Press.
18. M. Mrugalski, "Neural network based modelling of nonlinear systems in fault detection schemes," PhD thesis, University of Zielona Góra (2004), (In Polish).
19. Mrugalski, M., Arinton, E., and Korbicz, J. (2003a), "Dynamic GMDH type neural networks," in Advances in Soft Computing: Neural Networks and Soft Computing, eds. L. Rutkowski and J. Kacprzyk, New York: Physica-Verlag: Springer-Verlag Company, pp. 698-703.
20. Mrugalski, M., Arinton, E., and Korbicz, J. (2003b), "Systems identification with the GMDH neural networks: A multi-dimensional case," in Computer Science: Artifical Neural Nets and Genetic Algorithms, eds. D.W. Pearson, N.C. Steel and R.F. Albrecht, Wien, New York: Springer-Verlag Company.
21. Mrugalski, M., and Korbicz, J. (2005a), "Robust fault detection via GMDH neural networks," 16th IFAC World Congress, Prague: Czech Republic. CD-ROM.
22. Mrugalski, M., and Korbicz, J. (2005b), "Confidence estimation of GMDH neural networks via least square method," 11th IEEE International Conference, Methods and Models in Automation and Robotics, Poland: Miedzyzdroje. CD-ROM.
23. Mueller, J.E., and Lemke, F. (2000), Self-organising Data Maining, Hamburg: Libri.
24. Narendra, K.S., and Lewis, F.L. (2001), "Introduction to the special issue on neural network feedback control," Automatica, 37, 1147-1148.
25. Nelles, O. (2001), Non-linear Systems Identification. From Classical Approaches to Neural Networks and Fuzzy Models, Berlin: Springer.
26. Obuchowicz, A. (2003), Evolutionary Algorithms for Global Optimization and Dynamic System Diagnosis, Zielona Góra: Lubuskie Scientific Society.
27. Patton, R.J., and Chen, J. (2000), "On eigenstructure assignment for robust fault diagnosis," International Journal of Robust and Nonlinear Control, 10, 1193-1208.
28. Patton, R.J., Frank, P.M., and Clark, R.N. (2000), Issues of Fault Diagnosis for Dynamic Systems, Berlin: Springer-Verlag.
29. Patton, R.J., and Korbicz (eds.), J. (1999), "Advances in computational intelligence for fault diagnosis systems," International Journal of Robust Nonlinear Control, 9, 468-735, (special issue).
30. Patton, R.J, Korbicz, J., Witczak, M., and Faisel, J.U (2005), "Combined computational intelligence and analytical methods in fault diagnosis," in Intelligent Control Systems using Computational Intelligence Techniques, ed. A.E. Ruano, London: The Institution of Electrical Engineers, pp. 349-392. IEE.
31. Pham, D.T., and Xing, L. (1995), Neural Networks for Identification Prediction and Control, London: Springer-Verlag.
32. Scola, H.R., Nikoukah, R., and Delebecque, F. (2003), "Test signal design for failure detection: A linear programming approach," International Journal of Applied Mathematics and Computer Science, 13, 515-526.
33. Soderstrom, T., and Stoica, P. (1989), System Identification, Hemel Hempstead: Prentice-Hall International.
34. Walter, E., and Pronzato, L. (1997), Identification of Parametric Models from Experimental Data, Berlin: Springer-Verlag.
35. Wang, C., and Hill, D.J. (2006), "Learning from neural control," IEEE Transactions on Neural Networks, 17, 130-146.
36. Witczak, M. (2006), "Advances in model-based fault diagnosis with evolutionary algorithms and neural networks," International Journal of Applied Mathematics and Computer Science, 16, 85-99.
37. Witczak, M., Korbicz, J., Mrugalski, M., and Patton, R.J. (2006), "A GMDH neural network based approach to robust fault detection and its application to solve the DAMADICS benchmark problem," Control Engineering Practice, 14, 671-683.
38. Wouwer, A.V., Renotte, C., and Remy, M. (2003), "Application of stochastic approximation techniques in neural modelling and control," International Journal of Systems Science, 34, 851-863.
39. Van den Boom, T.J.J., Botto, M.A., and Hoekstra, P. (2005), "Design of an analytic constrained predictive controller using neural networks," International Journal of Systems Science, 36, 639-650.
40. Vieiraa, J., Diasb, F.M., and Motac, A. (2004), "Artificial neural networks and neuro-fuzzy systems for modelling and controlling real systems: a comparative study," Engineering Applications of Artificial Intelligence, 17, 265-273.