A Dynamic Weighted RBF-Based Ensemble for Prediction of Time Series Data from Nuclear Components



Published Nov 3, 2020
Jie Liu Valeria Vitelli Enrico Zio Redouane Seraoui


In this paper, an ensemble approach is proposed for prediction of time series data based on a Support Vector Regression (SVR) algorithm with RBF loss function. We propose a strategy to build diverse sub-models of the ensemble based on the Feature Vector Selection (FVS) method of Baudat & Anouar (2003), which decreases the computational burden and keeps the generalization performance of the model. A simple but effective strategy is used to calculate the weights of each data point for different sub-models built with RBF-SVR. A real case study on a nuclear power production component is presented. Comparisons with results given by the best single SVR model and a fixed-weights ensemble prove the robustness and accuracy of the proposed ensemble approach.

Abstract 147 | PDF Downloads 175



Ensemble, feature vector selection, dynamic weights calculation

Acar, E., & Rais-Rohani, M. (2009). Ensemble of metamodels with optimized weight factors. Structural and Multidisciplinary Optimization, 37(3), 279-294.
Bauer, E., & Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine learning, 36(1-2), 105-139.
Baudat, G., & Anouar, F. (2003). Feature vector selection and projection using kernels. Neurocomputing, 55(1-2), 21-38.
Bonissone, Piero P., Feng Xue, and Raj Subbu. "Fast meta-models for local fusion of multiple predictive models." Applied Soft Computing 11.2 (2011): 1529-1539.
Brodsky, J. B., Lemmens, H. J., Brock-Utne, J. G., Vierra, M., & Saidman, L. J. (2002). Morbid obesity and tracheal intubation. Anesthesia & Analgesia,94(3), 732-736.
Chen, S., Wang, W., & Van Zuylen, H. (2009). Construct support vector machine ensemble to detect traffic incident. Expert systems with applications, 36(8), 10976-10986.
Fantoni, Paolo F. "A neuro-fuzzy model applied to full range signal validation of PWR nuclear power plant data." INTERNATIONAL JOURNAL OF GENERAL SYSTEM 29.2 (2000): 305-320.
Gao, J. B., Gunn, S. R., Harris, C. J., & Brown, M. (2002). A probabilistic framework for SVM regression and error bar estimation. Machine Learning,46(1-3), 71-89.
Gurram, P., & Kwon, H. (2013). Sparse kernel-based ensemble learning with fully optimized kernel parameters for hyperspectral classification problems.Geoscience and Remote Sensing, IEEE Transactions on, 51(2), 787-802.
Hallbert B. & Thomas K. (2014), Advanced Instrumentation, Information, and Control Systems Technologies Technical Program Plan for 2014, Idaho National Laboratory, INL/EXT-13-28055 Rev. 3, September 2014.
Hu, C., Youn, B. D., Wang, P., & Taek Yoon, J. (2012). Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life. Reliability Engineering & System Safety, 103, 120-135.
Koo, In Soo, and Whan Woo Kim. "The development of reactor coolant pump vibration monitoring and a diagnostic system in the nuclear power plant." ISA transactions 39.3 (2000): 309-316.
Kim, H. C., Pang, S., Je, H. M., Kim, D., & Yang Bang, S. (2003). Constructing support vector machine ensemble. Pattern recognition, 36(12), 2757-2767.
Lee, Terence. "Assessment of safety culture at a nuclear reprocessing plant." Work & Stress 12.3 (1998): 217-237.
Liu, J., Seraoui, R., Vitelli, V., & Zio, E. (2013). Nuclear power plant components condition monitoring by probabilistic support vector machine. Annals of Nuclear Energy, 56, 23-33.
Masry, E. (1996). Multivariate local polynomial regression for time series: uniform strong consistency and rates. Journal of Time Series Analysis, 17(6), 571-599.
Minh, H. Q., Niyogi, P., & Yao, Y. (2006). Mercer’s theorem, feature maps, and smoothing. In Learning theory (pp. 154-168). Springer Berlin Heidelberg.
Muhlbaier, M. D., Topalis, A., & Polikar, R. (2009). Learn. NC: Combining Ensemble of Classifiers With Dynamically Weighted Consult-and-Vote for Efficient Incremental Learning of New Classes. Neural Networks, IEEE Transactions on, 20(1), 152-168.
Polikar, R. (2006). Ensemble based systems in decision making. Circuits and Systems Magazine, IEEE, 6(3), 21-45.
Quinlan J R. Bagging, boosting, and C4. 5[C]//AAAI/IAAI, Vol. 1. 1996: 725-730.
Razavi-Far, R., Baraldi, P., & Zio, E. (2012). Dynamic Weighting Ensembles for Incremental Learning and Diagnosing New Concept Class Faults in Nuclear Power Systems. Nuclear Science, IEEE Transactions on, 59(5), 2520-2530.
Smola, A. J., & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and computing, 14(3), 199-222.
Sollich, P. (1999). Probabilistic interpretations and Bayesian methods for support vector machines. In Artificial Neural Networks, 1999. ICANN 99. Ninth International Conference on (Conf. Publ. No. 470) (Vol. 1, pp. 91-96). IET.
Valentini, G., & Dietterich, T. G. (2003, August). Low bias bagged support vector machines. In ICML (pp. 752-759).
Yang, X., Yuan, B., & Liu, W. (2009, November). Dynamic Weighting Ensembles for incremental learning. In Proc. of IEEE conference in pattern recognition (pp. 1-5).
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