A strip thickness prediction method of hot rolling based on D_S information reconstruction
来源期刊：中南大学学报(英文版)2015年第6期
论文作者：SUN Li-jie SHAO Cheng ZHANG Li
文章页码：2192 - 2200
Key words：grey relational degree; GM(1,1) model; Dempster/Shafer (D_S) method; least square method; thickness prediction
Abstract: To improve prediction accuracy of strip thickness in hot rolling, a kind of Dempster/Shafer(D_S) information reconstitution prediction method (DSIRPM) was presented. DSIRPM basically consisted of three steps to implement the prediction of strip thickness. Firstly, ibaAnalyzer was employed to analyze the periodicity of hot rolling and find three sensitive parameters to strip thickness, which were used to undertake polynomial curve fitting prediction based on least square respectively, and preliminary prediction results were obtained. Then, D_S evidence theory was used to reconstruct the prediction results under different parameters, in which basic probability assignment (BPA) was the key and the proposed contribution rate calculated using grey relational degree was regarded as BPA, which realizes BPA selection objectively. Finally, from this distribution, future strip thickness trend was inferred. Experimental results clearly show the improved prediction accuracy and stability compared with other prediction models, such as GM(1,1) and the weighted average prediction model.
SUN Li-jie(孙丽杰)1, SHAO Cheng(邵诚)1, ZHANG Li(张利)2
(1. Institute of Advanced Control Technology, Dalian University of Technology, Dalian 116024, China;
2. School of Information, Liaoning University, Shenyang 110036, China)
Abstract:To improve prediction accuracy of strip thickness in hot rolling, a kind of Dempster/Shafer(D_S) information reconstitution prediction method (DSIRPM) was presented. DSIRPM basically consisted of three steps to implement the prediction of strip thickness. Firstly, ibaAnalyzer was employed to analyze the periodicity of hot rolling and find three sensitive parameters to strip thickness, which were used to undertake polynomial curve fitting prediction based on least square respectively, and preliminary prediction results were obtained. Then, D_S evidence theory was used to reconstruct the prediction results under different parameters, in which basic probability assignment (BPA) was the key and the proposed contribution rate calculated using grey relational degree was regarded as BPA, which realizes BPA selection objectively. Finally, from this distribution, future strip thickness trend was inferred. Experimental results clearly show the improved prediction accuracy and stability compared with other prediction models, such as GM(1,1) and the weighted average prediction model.
Key words:grey relational degree; GM(1,1) model; Dempster/Shafer (D_S) method; least square method; thickness prediction
