摘要：For predicting the key technology index of electroslag remelting (ESR) process (the melting rate and cone purification coefficient of the consumable electrode), a radial basis function (RBF) neural network soft-sensor model optimized by the artificial fish swarm algorithm (AFSA) is proposed. Based on the technique characteristics of ESR production process, the auxiliary variables of soft-sensor model are selected. Then the AFSA is adopted to train the RBF neural network prediction model in order to realize the nonlinear mapping between input and output variables. Simulation results show that the model has better generalization and prediction accuracy, which can meet the online soft sensing requirement of ESR process real-time control.
