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Science and Technology of Nuclear Installations
Volume 2008, Article ID 695153, 6 pages
http://dx.doi.org/10.1155/2008/695153
Research Article

Machine Learning of the Reactor Core Loading Pattern Critical Parameters

1Department of Applied Physics, Faculty of Electrical Engineering and Computing, Unska 3, 10000 Zagreb, Croatia
2Division of Electronics, Ruđer Bošković Institute, Bijenička 54, 10002 Zagreb, Croatia

Received 11 March 2008; Accepted 23 June 2008

Academic Editor: Igor Jencic

Copyright © 2008 Krešimir Trontl et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The usual approach to loading pattern optimization involves high degree of engineering judgment, a set of heuristic rules, an optimization algorithm, and a computer code used for evaluating proposed loading patterns. The speed of the optimization process is highly dependent on the computer code used for the evaluation. In this paper, we investigate the applicability of a machine learning model which could be used for fast loading pattern evaluation. We employ a recently introduced machine learning technique, support vector regression (SVR), which is a data driven, kernel based, nonlinear modeling paradigm, in which model parameters are automatically determined by solving a quadratic optimization problem. The main objective of the work reported in this paper was to evaluate the possibility of applying SVR method for reactor core loading pattern modeling. We illustrate the performance of the solution and discuss its applicability, that is, complexity, speed, and accuracy.