Forecasting of Steel Demands by Using Support Vector -Machine and Immune Algorithm

Abstract

Abstract: It is very important for the Iron and Steel industry to forecast the steel demands. In order to improve the forecasting accuracy, a method by using support vector machine combined with immune algorithm (IA-SVM) is proposed. By this method, to obtain an effective SVM model, the parameters in the SVM are optimized by using immune algorithm. In the parameter optimization, a new policy called group updating is introduced to improve its convergence speed and performance. With a data set of steel demands in China from 1990 to 2009, empirical analysis is carried out. Results show that the proposed IA-SVM is effective. 

Key words: support vector machine, immune algorithm, steel demand, forecasting

Yan Rui, Zhang Qun. Forecasting of Steel Demands by Using Support Vector -Machine and Immune Algorithm[J]. Industrial Engineering Journal , 2013, 16(5): 90-95.

References

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