烧结矿碱度的灰色神经网络预测模型及仿真

本文提出了用灰色神经网络对烧结矿化学成分进行预测，并在此基础上构造了灰色神经网络模型，该模型有效地融合了灰色理论可弱化数据序列波动性和神经网络特有的适应非线性信息处理的能力，研究结果证明，本模型能在小样本、贫信息的条件下对烧结矿碱度做出比较准确的预测，此种模型具有预测精度高、所需样本少、计算简便等优点，取得了比较满意的结果。和BP神经网络算法相比，灰色神经网络算法有很大的应用前景和推广价值。

Prediction Model of the Sinter Basicity Based on Grey Neural Network Algebra

A grey neural network model was proposed for sinter chemical components prediction. On the basis of the model, the fluctuation of data sequence is weakened by the grey theory and the neural network is capable of processing nonlinear adaptable information, and the GNN is a combination of those advantages. The results reveal that, the sinter basicity can be accurately predicted through this model by reference to small sample and information. It was concluded that the GNN model is effective with the advantages of high precision, less samples required and simple calculation.