An intelligent integrated-prediction model for components of Pb-Zn agglomerate based on the process neural network(PNN) and the improved grey system(IGS)
基于PNN和IGS的铅锌烧结块成分智能集成预测模型

To deal with the problem of the component prediction for Pb-Zn agglomerate, an intelligent integratedprediction model based on the process neural network(PNN) and the improved grey system(IGS) is presented. First, the component of agglomerate is predicted by PNN and IGS models, and then, a recursive entropy algorithm for the weighting coefficients is devised from the viewpoint of the information theory. The component of Pb-Zn agglomerate is predicted by integrating the two prediction models. Application results show that the integrated model has high prediction accuracy; it predicts the components of agglomerate efficiently and meets the data-completeness requirements for proportioning computation.