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http://dx.doi.org/10.5516/NET.2009.41.10.1293

MODELLING THE DYNAMICS OF THE LEAD BISMUTH EUTECTIC EXPERIMENTAL ACCELERATOR DRIVEN SYSTEM BY AN INFINITE IMPULSE RESPONSE LOCALLY RECURRENT NEURAL NETWORK  

Zio, Enrico (Energy Department, Polytechnic of Milan)
Pedroni, Nicola (Energy Department, Polytechnic of Milan)
Broggi, Matteo (Energy Department, Polytechnic of Milan)
Golea, Lucia Roxana (Energy Department, Polytechnic of Milan)
Publication Information
Nuclear Engineering and Technology / v.41, no.10, 2009 , pp. 1293-1306 More about this Journal
Abstract
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.
Keywords
Locally Recurrent Neural Network; Nuclear Reactor; Nonlinear Dynamics; Transient Recovery; Transient Interpolation; Transient Extrapolation;
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