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ENRICO ZIO,NICOLA PEDRONI,MATTEO BROGGI,LUCIA ROXANA GOLEA 한국원자력학회 2009 Nuclear Engineering and Technology Vol.41 No.10
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.
Zio, Enrico,Pedroni, Nicola,Broggi, Matteo,Golea, Lucia Roxana Korean Nuclear Society 2009 Nuclear Engineering and Technology Vol.41 No.10
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.