Published December 2009
| Version v1
Journal article
Modeling the dynamics of the lead bismuth eutectic experimental accelerator driven system by an infinite impulse response locally recurrent neural network
- 1. Polytechnic of Milan, Milan (Italy)
Description
In this paper, an infinite impulse response locally recurrent neural network (IIR-LRNN) is employed for modelling the dynamics of the Lead Bismuth Eutectic eXperimental Accelerator Driven System (LBE-XADS). The network is trained by recursive back-propagation (RBP) and its ability in estimating transients is tested under various conditions. The results demonstrate the robustness of the locally recurrent scheme in the reconstruction of complex nonlinear dynamic relationships
Additional details
Publishing Information
- Journal Title
- Nuclear Engineering and Technology
- Journal Volume
- 41
- Journal Issue
- 10
- Series
- 16 refs, 19 figs, 3 tabs
- Journal Page Range
- p. 1293-1306
- ISSN
- 1738-5733
INIS
- Country of Publication
- Korea, Republic of
- Country of Input or Organization
- Korea, Republic of
- INIS RN
- 41094078
- Subject category
- S21: SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS; S43: PARTICLE ACCELERATORS;
- Descriptors DEI
- ACCELERATORS; BISMUTH; EUTECTICS; NEURAL NETWORKS; SIMULATION; TRANSIENTS
- Descriptors DEC
- ELEMENTS; METALS