Ordered Neural Network and its Application to Prediction of Anode Effect

Ordered neural network(ONN) is applied to prediction of anode effect(AE) in aluminium electrolysis cell.The neural network(NN) and other methods in aluminum electrolysis cell fault diagnosis are reviewed,and the comparative advantages of NN method are analyzed.ONN topology structure is introduced and learning algorithm is derived.Improvements from traditional backpropagation NN(BPNN) to ONN are illuminated.Eventually,correctness and practicality of the application is validated.ONN and some typical NNs are trained and tested with using real data from aluminum electrolysis plant.In contrast with other NNs,ONN can predict aluminum electrolysis cell AE more timely and rightly.