Application of WASVM Combined Model to Predict Silicon Content in Hot Metal
WANG Yikang1,GAO Chuanhou2
1. College of Science, China Jiliang University, Hangzhou 310018, Zhejiang, China; 2. Department of Mathematics, Zhejiang University, Hangzhou 310027, Zhejiang, China
Abstract：Based on the fact that wavelet is suitable for processing nonlinear, nonstationary random signals and support vector machine excel at solving nonlinear, smallsample, high dimensional problems, the paper proposes a combined model of wavelet analysis (WA) and support vector machine (SVM). It decomposes the time series of original silicon content in hot metal to different layers through wavelet analysis. Different SVMs are built to predict each layer, and finally to obtain the predicted results of the original time series by composition. Taking No.1 blast furnace of Laiwu Iron and Steel Group Co. as an example, application result shows that the result of WASVM model is better than that of the AR model frequently applied in the project and the single least squares support vector machine and the prediction accuracy is elevated obviously.
王义康1 ,郜传厚2. 基于WASVM模型的高炉铁水含硅量预测[J]. 中国冶金, 2009, 19(4): 8-8.
WANG Yikang1,GAO Chuanhou2. Application of WASVM Combined Model to Predict Silicon Content in Hot Metal. China Metallurgy, 2009, 19(4): 8-8.
