Abstract
The complexity and reliability demands of contemporary industrial systems and technological processes require the development of new fault diagnosis approaches. Performance results for finding the best genetic algorithm for the complex real problem of optimal machinery equipment operation and predictive maintenance are presented. A genetic algorithm is a stochastic computational model that seeks the optimal solution to an objective function. A methodology calculation is based on the idea of measuring the increase of fitness and fitness quality evaluation with chaos theory principles applying within genetic algorithm environment. Fuzzy neural networks principles are effectively applied in solved manufacturing problems mostly where multisensor integration, real-timeness, robustness and learning abilities are needed. A modified Mamdani neuro-fuzzy system improves the interpretability of used domain knowledge.
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References
Gallová, Š.: Some effective techniques applying for complex diagnostic problem solving via genetic algorithm approach. In: Computational Intelligence: Methods and Applications, pp. 183–207. EXIT, Warsaw, ISBN 978-83-60434-50-5 (2008)
Abraham, NB., Lugiato, L.A., Narducci, L.M.: Instabilities in active media. J. Soc. Am. B. (1999)
Korytkowski, M., Rutkowski, L., Scherer, R.: On combining backpropagation with boosting. In: Proceedings of International Joint Conference on Neural Networks, IEEE World Congress on Computational Intelligence, Vancouver, Canada (2006)
Wang, L.: Adaptive Fuzzy Systems and Control. PTR Prentice-Hall, Englewood Cliffs, NJ (1994)
Mrugalski, M.: Neural Network Based Modelling of Non-Linear Systems. University of Zielona Gora, Poland (2004)
Li, Z., Park, J.B., Joo, I.H., Chen, G., Choi, I.H.: Anticontrol of chaos for discrete TS fuzzy systems. IEEE Trans. Circ. Syst. 1 49 (2) (2002)
Gallová, Š.: A maximum entropy inference within uncertain information reasoning. In: Information Processing and Management of Uncertainty in Knowledge-based Systems: Proceedings, pp. 1803–1810, Paris, Les Cordeliers, E.D.K., Paris, 2–7 July 2006, ISBN sss-X (2006)
Mandel, P., Erneux, T.: Dynamic versus static stability. In: Hilger, A (ed.) Frontiers in Quantum Optics, Bristol, Boston, MA (1986)
Badii, R., Politi, A.: Strange attractors. Phys. Lett. 104A, 303 (1984)
Goldman, S.A., Rivest, R.L.: A non-iterative maximum entropy algorithm. In: Koval, L.N., Lemmu, F.J. (eds.) Ucertainty in Artficial Intelligence, Vol. 2, pp. 133–148, North-Holland (1988)
Hamming, R.W.: Coding and Information Theory, Prentice-Hall, Englewood Cliffs, NJ (1980)
Ballé, P.: Fuzzy model-based parity equations for fault isolation. Contr. Eng. Prac. 7, 261–270 (1999)
Zhang, J., Roberts, P.D.: On-Line Process Fault Diagnosis Using Neural Network Techniques, Institute of Measurement and Control (1992)
Montana, D., Davis, L.: Training feedforward neural networks using genetic algorihms. In: Proceedings of the 11th International Joint Coference on Artificial Intelligence, pp. 762–767 (1989)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, MA (1989)
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Gallova, S. (2010). Diagnostic Problem Solving by Means of Neuro-Fuzzy Learning, Genetic Algorithm and Chaos Theory Principles Applying. In: Ao, SI., Gelman, L. (eds) Electronic Engineering and Computing Technology. Lecture Notes in Electrical Engineering, vol 60. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-8776-8_33
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DOI: https://doi.org/10.1007/978-90-481-8776-8_33
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