基于灰色神经网络的烧结碱度预测与仿真

PREDICTION OF R IN SINTER PROCESS BASED ON GREY NEURAL NETWORK ALGEBRA

建立了应用灰色神经网络对烧机矿化学成分进行预测的有关理论,并在此基础上构造了灰色神经网络模型。该模型中,灰色理论弱化数据序列波动性和神经网络特有的非线性适应性信息处理能力相融合,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。该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。

A grey neural network model was proposed on the basis of the models.The fluctuation of data sequence is weakened by the grey theory and the neural network is capable of processing non-linear adaptable information, and the GNN is a combination of those advantages. The results reveal, the alkalinity of sinter 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.