Development of a general learning algorithm with applications in nuclear reactor systems [electronic resource].
- Washington, D.C. : United States. Office of the Assistant Secretary for Nuclear Energy, 1989.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy
- Physical Description:
- Pages: (97 pages) : digital, PDF file
- Additional Creators:
- Oak Ridge National Laboratory, United States. Office of the Assistant Secretary for Nuclear Energy, and United States. Department of Energy. Office of Scientific and Technical Information
- Restrictions on Access:
- Free-to-read Unrestricted online access
- The objective of this study was development of a generalized learning algorithm that can learn to predict a particular feature of a process by observation of a set of representative input examples. The algorithm uses pattern matching and statistical analysis techniques to find a functional relationship between descriptive attributes of the input examples and the feature to be predicted. The algorithm was tested by applying it to a set of examples consisting of performance descriptions for 277 fuel cycles of Oak Ridge National Laboratory's High Flux Isotope Reactor (HFIR). The program learned to predict the critical rod position for the HFIR from core configuration data prior to reactor startup. The functional relationship bases its predictions on initial core reactivity, the number of certain targets placed in the center of the reactor, and the total exposure of the control plates. Twelve characteristic fuel cycle clusters were identified. Nine fuel cycles were diagnosed as having noisy data, and one could not be predicted by the functional relationship. 13 refs., 6 figs.
- Report Numbers:
- E 1.99:ornl/tm-10650
- Published through SciTech Connect.
Brittain, C.R.; Otaduy, P.J.; Perez, R.B.
- Funding Information:
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