Improved nuclear reactor temperature control using diagonal recurrent neural networks

A novel approach to wide-range optimal reactor temperature control using diagonal recurrent neural networks (DRNNs) with an adaptive learning rate scheme is presented. The drawback of the usual feedforward neural network (FNN) is that it is a static mapping and requires a large number of neurons and takes a long training time. The usual fixed learning rate based on an empirical trial and error scheme is slow and does not guarantee convergence. The DRNN is for dynamic mapping and requires much fewer neurons and weights, and thus converges faster than FNN. A dynamic backpropagation algorithm coupled with an adaptive learning rate guarantees even faster convergence. The DRNN controller described includes both a neurocontroller and a neuroidentifier. A reference model which incorporates an optimal control law with improved reactor temperature response is used for training of the neurocontroller and neuroidentifier. Rapid convergence of this DRNN-based control system is demonstrated when used to improve reactor temperature performance