| High Precision Prediction of Rolling Force Based on Fuzzy and Nerve Method for Cold Tandem Mill |
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| 作者姓名： | JIA Chunyu SHAN Xiuying NIU Zhaoping |
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| 作者单位： | JIA Chun-yu（College of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, Hebei, China） ;
SHAN Xiu-ying（College of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, Hebei, China） ;
NIU Zhao-ping（College of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, Hebei, China） ; |
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| 基金项目： | 河北省自然科学基金，国家自然科学基金 |
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| 摘 要： | The rolling force model for cold tandem mill was put forward by using the Elman dynamic recursive network method,based on the actual measured data.Furthermore,a good assumption is put forward,which brings a full universe of discourse self-adjusting factor fuzzy control,closed-loop adjusting,based on error feedback and expertise into a rolling force prediction model,to modify prediction outputs and improve prediction precision and robustness.The simulated results indicate that the method is highly effective and the prediction precision is better than that of the traditional method.Predicted relative error is less than ±4%,so the prediction is high precise for the cold tandem mill.
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| 关 键 词： | 动力学循环网络 模糊控制 冷串联机 压制能量 |
| 收稿时间： | 1900-01-01; |
High Precision Prediction of Rolling Force Based on Fuzzy and Nerve Method for Cold Tandem Mill |
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| JIA Chun-yu,SHAN Xiu-ying,NIU Zhao-ping.High Precision Prediction of Rolling Force Based on Fuzzy and Nerve Method for Cold Tandem Mill[J].Journal of Iron and Steel Research,2008,15(2):23-0. |
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| Authors: | JIA Chun-yu SHAN Xiu-ying NIU Zhao-ping |
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| Affiliation: | College of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, Hebei, China |
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| Abstract: | The rolling force model for cold tandem mill was put forward by using the Elman dynamic recursive network method, based on the actual measured data. Furthermore, a good assumption is put forward, which brings a full universe of discourse self-adjusting factor fuzzy control, closed-loop adjusting, based on error feedback and expertise into a rolling force prediction model, to modify prediction outputs and improve prediction precision and robustness. The simulated results indicate that the method is highly effective and the prediction precision is better than that of the traditional method. Predicted relative error is less than ±4%, so the prediction is high precise for the cold tandem mill. |
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| Keywords: | Elman dynamic recursive network fuzzy control cold tandem mill rolling force prediction |
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