An Improved Artificial Neural Network Model for Predicting Silicon Content of Blast Furnace Hot Metal

作     者：Bin Yao, Tianjun Yang, Xiaojun Ning (Metallurgy School, University of Science and Technology Beijing, Beijing 100083, China) 

作者机构：Metallurgy School University of Science and Technology Beijing Beijing 100083 China 

出 版 物：《International Journal of Minerals,Metallurgy and Materials》 (矿物冶金与材料学报（英文版）)

年 卷 期：2000年第14卷第4期

页      面：269-272页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：blast furnace silicon content neural network Metallurgy 

摘      要：Based on the skills of initializing weight distribution, adding an impulse in a neural network and expanding the ideal of plural weights, an artificial neural network model with three connection weights between one and another neural unit was established to predict silicon content of blast furnace hot metal. After the neural network was trained in the off-line state on the basis of a large number of practical data of a commercial blast furnace and making many learning patterns, satisfactory testing and simulating results of the model were obtained.