Heuristic modeling using recurrent neural networks: simulated and real-data experiments
Keywords:
chaotic dynamic systems, recurrent neural networks, gradient-based and soft computing learning algorithms, nonlinear system identification, time-series forecastingAbstract
The focus of this paper is on the problems of system identification, process modeling and time series forecasting which can be met during the use of locally recurrent neural networks in heuristic modelling technique. However, the main interest of this paper is to survey the properties of the dynamic neural processor which is developed by the author. Moreover, a comparative study of selected recurrent neural architectures in modeling tasks is given. The results of experiments showed that some processes tend to be chaotic and in some cases it is reasonable to use soft computing models for fault diagnosis and control.
References
[1] M. Ayoubi. Nonlinear dynamic systems identification with dynamic neural networks for fault diagnosis in technical processes. In Humans, Information and Technology, 3: 2120-2125, 2-5 October 1994.
[2] M. Blanke, M. Kinnaert, J. Lunze, M. Staroswiecki. Diagnosis and Fault-Tolerant Control. Springer-Verlag, Berlin-Heidelberg, 2003.
[3] W. Duch, J. Korbicz, L. Rutkowski, R. Tadeusiewicz. Sieci neuronowe, vol. 6 of Biocybernetyka i inżynieria biomedyczna. Akademicka Oficyna Wydawnicza EXIT, Warszawa, 2000. '
[4] K. Gobbak, H. Raghavendran, M. Tapas. Internal feedback neuron networks for modeling of an industrial furnace. In Neural Networks, pp. 700-705. IEEE, 1997.
[5] S. Graziani, N. Pitrone, M.G. Xibilia, N. Barbalace. Improving monitoring of NO.ᵪ, emissions in refineries. In Instrumentation and Measurement Technology Conference, Proceedings of the 21st IEEE, vol. 1, pp. 594-597, 18- 20 May 2004.
[2] M. Blanke, M. Kinnaert, J. Lunze, M. Staroswiecki. Diagnosis and Fault-Tolerant Control. Springer-Verlag, Berlin-Heidelberg, 2003.
[3] W. Duch, J. Korbicz, L. Rutkowski, R. Tadeusiewicz. Sieci neuronowe, vol. 6 of Biocybernetyka i inżynieria biomedyczna. Akademicka Oficyna Wydawnicza EXIT, Warszawa, 2000. '
[4] K. Gobbak, H. Raghavendran, M. Tapas. Internal feedback neuron networks for modeling of an industrial furnace. In Neural Networks, pp. 700-705. IEEE, 1997.
[5] S. Graziani, N. Pitrone, M.G. Xibilia, N. Barbalace. Improving monitoring of NO.ᵪ, emissions in refineries. In Instrumentation and Measurement Technology Conference, Proceedings of the 21st IEEE, vol. 1, pp. 594-597, 18- 20 May 2004.
Published
2022-08-17
Issue
pp. 715-727
Section
Articles
License
Copyright © The Author(s).
This work is licensed under the Creative Commons Attribution 4.0 International CC BY 4.0.
How to Cite
Przystałka, P. (2022). Heuristic modeling using recurrent neural networks: simulated and real-data experiments. Computer Assisted Methods in Engineering and Science, 14(4), 715-727. https://cames3.ippt.pan.pl/index.php/cames/article/view/806
