Abstract
In this paper, we propose two different neural network (NN) approaches for industrial process signal forecasting. Real data is available for this research from boiling water reactor type nuclear power reactors. NNs are widely used for time series prediction, but it isn’t utilized for Olkiluoto nuclear power plant (NPP), Finland. Preprocessing, suitable input signals and delay analysis are important phases in modelling. Optimized number of delayed input signals and neurons in hidden-layer are found to make possible prediction of idle power process signal. It is mainly concentrated on algorithms on input signal selection and finding the optimal model for one-step ahead prediction.
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References
Haykin, S.: Neural networks: a comprehensive foundation. Prentice Hall PTR, Upper Saddle River (1994)
Ding, S., Jia, W., Su, C., Xu, X., Zhang, L.: PCA-Based Elman Neural Network Algorithm. Advances in Computation and Intelligence, 315–321 (2008)
Talonen, J., Sirola, M.: Abnormal Process State Detection by Cluster Center Point Monitoring in BWR Nuclear Power Plant. In: Proceedings of the International Conference on Data Mining, DMIN, pp. 247–252 (2009)
Sirola, M., Talonen, J., Lampi, G.: SOM based methods in early fault detection of nuclear industry. In: Proceedings of the 17th European Symposium On Artificial Neural Networks, ESANN (2009)
Hu, Q., Xie, M., Ng, S., Levitin, G.: Robust recurrent neural network modeling for software fault detection and correction prediction. Reliability Engineering & System Safety 92(3), 332–340 (2007)
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Talonen, J., Sirola, M., Augilius, E. (2010). Modelling Power Output at Nuclear Power Plant by Neural Networks. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15819-3_6
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DOI: https://doi.org/10.1007/978-3-642-15819-3_6
Publisher Name: Springer, Berlin, Heidelberg
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