| 引用本文: | 高山凤,刘鸿飞,郗安民,杨贤.热轧板带横向厚度分布的预测与控制[J].哈尔滨工业大学学报,2016,48(1):180.DOI:10.11918/j.issn.0367-6234.2016.01.027 |
| GAO Shanfeng,LIU Hongfei,XI Anmin,YANG Xian.Prediction and control of thickness transverse distribution in hot rolling strip[J].Journal of Harbin Institute of Technology,2016,48(1):180.DOI:10.11918/j.issn.0367-6234.2016.01.027 |
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| 摘要: |
| 针对板带热轧过程中终轧板带横向厚度分布的检测、预测方法存在的缺陷，建立自适应粒子群优化算法（PSO）和误差反传递（BP）算法混合训练的神经网络预测模型.该网络模型在BP神经网络的基础上，通过自学习过程对网络结构进行动态优化；借助PSO算法优化网络的权值和阈值，提高网络收敛速度和预测精度.某厂二辊可逆热轧机现场轧制数据验证表明：稳态轧制状态下，该模型预测精度高，平均绝对误差仅为3.6 μm，其中87.1%的误差在±4 μm范围内；通过对轧后板带横向厚度的统计分析，去除板带头尾部分，板带厚度的绝对误差在30 μm以内的频率为90%.该神经网络模型可以代替凸度仪对热轧板带横向厚度分布进行预测，并且能够对板形的调控机构根据预测结果进行精确的控制，适应高精度板形控制的要求.
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| 关键词: 热轧 板厚 神经网络 预测 粒子群优化算法 |
| DOI：10.11918/j.issn.0367-6234.2016.01.027 |
| 分类号:TG333.7 |
| 文献标识码:A |
| 基金项目:内蒙古自治区战略性新兴产业专项(2012). |
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| Prediction and control of thickness transverse distribution in hot rolling strip |
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GAO Shanfeng, LIU Hongfei, XI Anmin, YANG Xian
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( School of Mechanical Engineering, University of Science and Technology Beijing, 100083 Beijing, China)
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| Abstract: |
| To solve the problem existing in the measuring and prediction of transverse thickness distribution of finish hot rolling, the adaptive neural network trained by hybrid algorithms of particle swarm optimization (PSO) and back propagation (BP) neural network is introduced. Based on the BP network, the network structure, weights and threshold are optimized by PSO algorithm for improving the network convergence speed and prediction accuracy. By the data of two high reversible hot rolling mill, the average error of thickness is 3.6 μm and the error absolute value is less than 4 μm accounted for 87.1%. The absolute error frequency of strip thickness within 30 μm is 90% by statistical analysis of the steady state rolling, excepting the head and tail of the strip. The research results show that the network model can replace crown instrument to predict the transverse thickness distribution in the actual production. And the control means of strip shape are precisely controlled. It illustrates that the network model can meet the requirement of high precision flatness control.
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| Key words: hot rolling thickness neural network forecast particle swarm optimization (PSO) |
