Robust sensor and actuator fault diagnosis with GMDH neural networks

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

Volumn 7902 LNCS, Issue PART 1, 2013, Pages 96-105

  Witczak, Marcin ^a   Mrugalski, Marcin ^a   Korbicz, Józef ^a  

^a UNIVERSITY OF ZIELONA GÓRA   (Poland)

Author keywords

Non linear system identification; Robust fault diagnosis; State space GMDH neural networks

Indexed keywords

GMDH NEURAL NETWORK; MODEL UNCERTAINTIES; NEURAL MODELS; NON-LINEAR SYSTEM IDENTIFICATION; ROBUST FAULT DETECTION AND ISOLATION; ROBUST FAULT DIAGNOSIS; ROBUST SENSOR; STATE SPACE REPRESENTATION;

UNCERTAINTY ANALYSIS;

NEURAL NETWORKS;

EID: 84880054262     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-38679-4_8     Document Type: Conference Paper

References (20)

1. Ding, S.: Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools. Springer, Heidelberg (2008)
2. Korbicz, J., Koscielny, J.: Modeling, Diagnostics and Process Control: Implementation in the DiaSter System. Springer, Berlin (2010)
3. Mrugalski, M.: An unscented kalman filter in designing dynamic gmdh neural networks for robust fault detection. International Journal of Applied Mathematics and Computer Science 23(1), 157-169 (2013)
4. Mrugalski, M., Witczak, M.: State-space gmdh neural networks for actuator robust fault diagnosis. Advances in Electrical and Computer Engin. 12(3), 65-72 (2012)
5. De Oca, S., Puig, V., Witczak, M., Dziekan, L.: Fault-tolerant control strategy for actuator faults using lpv techniques: Application to a two degree of freedom helicopter. International Journal of AppliedMathematics and Computer Science 22(1), 161-171 (2012)
6. Niemann, H.: A model-based approach to fault-tolerant control. International Journal of Applied Mathematics and Computer Science 22(1), 67-86 (2012)
7. Noura, H., Theilliol, D., Ponsart, J., Chamseddine, A.: Fault-tolerant Control Systems: Design and Practical Applications. Springer, London (2009)
8. Haykin, S.: Neural Networks and Learning Machines. Prentice Hall, NY (2009)
9. Mrugalski, M., Witczak, M., Korbicz, J.: Confidence estimation of the multi-layer perceptron and its application in fault detection systems. Engineering Applications of Artificial Intelligence 21(6), 895-906 (2008)
10. Patan, K., Witczak, M., Korbicz, J.: Towards robustness in neural network based fault diagnosis. International Journal of Applied Mathematics and Computer Science 18(4), 443-454 (2008)
11. Ivakhnenko, A., Mueller, J.: Self-organization of nets of active neurons. System Analysis Modelling Simulation 20, 93-106 (1995)
12. Korbicz, J., Mrugalski, M.: Confidence estimation of gmdh neural networks and its application in fault detection system. International Journal of System Science 39(8), 783-800 (2008)
13. Witczak, M., Korbicz, J., Mrugalski, M., Patton, R.: A gmdh neural network based approach to robust fault detection and its application to solve the damadics benchmark problem. Control Engineering Practice 14(6), 671-683 (2006)
14. Teixeira, B., Torres, L., Aguirre, L., Bernstein, D.: On unscented kalman filtering with state interval constraints. Journal of Process Control 20(1), 45-57 (2010)
15. Korbicz, J., Witczak, M., Puig, V.: Lmi-based strategies for designing observers and unknown input observers for non-linear discrete-time systems. Bulletin of the Polish Academy of Sciences: Technical Sciences 55(1), 31-42 (2007)
16. Witczak, M., Pretki, P.: Design of an extended unknown input observer with stochastic robustness techniques and evolutionary algorithms. International Journal of Control 80(5), 749-762 (2007)
17. Zemouche, A., Boutayeb, M., Iulia Bara, G.: Observer for a class of Lipschitz systems with extension to H? performance analysis. Systems and Control Letters 57(1), 18-27 (2008)
18. Mrugalski, M., Arinton, E., Korbicz, J.: Dynamic gmdh type neural networks. In: Rutkowski, L., Kacprzyk, J. (eds.) Neural Networks and Soft Computing, pp. 698-703. Physica-Verlag, Heidelberg (2003)
19. Mrugalski, M., Korbicz, J.: Least mean square vs. outer bounding ellipsoid algorithm in confidence estimation of the gmdh neural networks. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds.) ICANNGA 2007, Part II. LNCS, vol. 4432, pp. 19-26. Springer, Heidelberg (2007)
20. Lee, T., Jiang, Z.: On uniform global asymptotic stability of nonlinear discretetime systems with applications. IEEE Trans. Automatic Control 51(10), 1644-1660 (2006)