Application of neural networks to signal prediction in nuclear power plant

The feasibility of using an artificial neural network for signal prediction is studied. The purpose of signal prediction is to estimate the value of the undetected next-time-step signal. In the prediction method, which is based on the idea of autoregression, a few previous signals are input to the artificial neural network, and the signal value of next time step is estimated from the outputs of the network. The artificial neural network can be applied to a nonlinear system and has fast response. The training algorithm is a modified backpropagation model, which can effectively reduce the training time. The target signal of the simulation is the steam generator water level in a nuclear power plant. The simulation result shows that the predicted value follows the real trend well.<
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