Study on prediction of sintering drum strength under small sample lacking information
Qiang Song1, Ai-min Wang2
COMPUTER MODELLING & NEW TECHNOLOGIES 2014 18(4) 278-283
1Mechanical Engineering Department, Anyang Institute of Technology, Anyang 455000, Henan, China
2Computer and Information Engineering College, Anyang Normal University, Anyang 455000, Henan, China
The paper provides a grey model and support vector machine algorithm and method for prediction of sinter drum strength based on the characteristics of large time delay, strong coupling, nonlinear, sintering process, put forward a kind of Combination forecasting model of drum strength based on grey model and support vector machine, the drum strength of sinter ore Laboratory values as output variables, the variables associated with the drum strength of sinter as input variables, using support vector machine powerful machine learning method and strong nonlinear fitting ability, so as to establish a stable, high precision of drum strength, the drum strength stronger generalization ability of the forecasting model, the method of the method has the high prediction accuracy, fast and convenient, and has great popularization and application value, and lay a good foundation for the green sintering technology of sintering.