Prediction of Liquid Sodium Flow Rate through the Core of the IBR-2M Reactor Using Nonlinear Autoregressive Neural Networks
This paper presents an artificial neural network method for long-term prediction of liquid sodium flow rate through the core of the IBR-2M reactor. The nonlinear autoregressive neural network (NAR) with local feedback connection has been considered as the most appropriate tool for such a prediction....
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2016-01-01
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| Series: | EPJ Web of Conferences |
| Online Access: | http://dx.doi.org/10.1051/epjconf/201610802036 |
| Summary: | This paper presents an artificial neural network method for long-term prediction of liquid sodium flow rate through the core of the IBR-2M reactor. The nonlinear autoregressive neural network (NAR) with local feedback connection has been considered as the most appropriate tool for such a prediction. The predicted results were compared with experimental values. NAR model predicts slow changes of liquid sodium flow rate up to two days with an error less than 5%. |
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| ISSN: | 2100-014X |