Abstract
This paper presents a hybrid Support Vector Machine (SVM) and Particle Swarm Optimization (PSO) model for predicting alpha particles emitting contamination on the internal surfaces of decommissioned channels. Six measuring parameters (channel diameter, channel length, distance to radioactive source, radioactive strength, wind speed and flux) and one ionizing value have been obtained via experiments. These parameters show complex linear and nonlinear relationships to measuring results. The model used PSO to optimize SVM parameters. The comparison of computational results of the hybrid approach with normal BP networks confirms its clear advantage for dealing with this complex nonlinear prediction.
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Liu, M., Tuo, X., Ren, J., Li, Z., Wang, L., Yang, J. (2012). A PSO-SVM Based Model for Alpha Particle Activity Prediction Inside Decommissioned Channels. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7367. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31346-2_58
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DOI: https://doi.org/10.1007/978-3-642-31346-2_58
Publisher Name: Springer, Berlin, Heidelberg
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