改进型支持向量回归预测模型的轧机轧制力预测
|
摘要
对轧机轧制力预测模型进行研究。使用人工鱼群优化算法对支持向量回归(SVR)参数选取进行最优的参数组合,将粒子群优化算法引入到常规人工鱼群算法中,并对其进行改进,提高了人工鱼群算法的性能。研究结果表明:Ekelund模型的轧制力计算结果误差较大,超过了10%,常规SVR预测模型的轧制力预测精度低于10%,而本文研究的改进SVR预测模型得到的轧制力误差低于5%,说明通过人工鱼群算法优化SVR算法模型的参数能够提高预测模型的预测精度,并且预测消耗时间在3种预测模型中是最短的。
Rolling force prediction model is studied. Use artificial fish swarm optimization algorithm for SVR parameters selection of the optimal parameters combination and the particle swarm optimization algorithm is introduced to conventional artificial fish swarm algorithm,and it is improved,to improve the performance of the artificial fish swarm algorithm. Research results show that the rolling force calculation results error of Ekelund model are larger than 10 %. The rolling force prediction precision of conventional SVR forecasting model is less than 10 %,and the rolling force error obtained by the improved SVR prediction model is lower than 5 %. Through the artificial fish swarm algorithm,the parameters of the SVR algorithm model can improve the prediction precision of the prediction model,and the consumption time is the shortest in the three prediction models.
Rolling force prediction model is studied. Use artificial fish swarm optimization algorithm for SVR parameters selection of the optimal parameters combination and the particle swarm optimization algorithm is introduced to conventional artificial fish swarm algorithm,and it is improved,to improve the performance of the artificial fish swarm algorithm. Research results show that the rolling force calculation results error of Ekelund model are larger than 10 %. The rolling force prediction precision of conventional SVR forecasting model is less than 10 %,and the rolling force error obtained by the improved SVR prediction model is lower than 5 %. Through the artificial fish swarm algorithm,the parameters of the SVR algorithm model can improve the prediction precision of the prediction model,and the consumption time is the shortest in the three prediction models.
引文
[1]董敏,刘才,李国友.轧制力预报问题中动态网络模型的实现[J].钢铁,2006,41(12):49-50.
[2]孙一康.带钢冷连轧计算机控制[M].北京:冶金工业出版社,2002.
[3]马更生,彭文,邸洪双,等.带钢热连轧换规格轧制力自学习优化[J].东北大学学报:自然科学版,2015,36(12):1715-1718.
[4]刘俊,杨荃,何安瑞,等.贝叶斯框架下的LS-SVM在热连轧机轧制力预报中的应用[J].钢铁研究学报,2011,23(6):5-8.
[5]刘莉,李传峰,张广军.基于BP神经网络斜轧穿孔轧制力的预测[J].山东冶金,2013,35(2):43-44.
[6]薛涛,杜凤山,孙静娜,等.基于FEM-ANN的冷轧带钢轧制力预报[J].中南大学学报:自然科学版,2013,44(11):4456-4460.
[7]杨景明,孙晓娜,车海军,等.基于蚁群算法的神经网络冷连轧机轧制力预报[J].钢铁,2009,44(3):52-55.
[8]吴东升,王大志,杨青,等.基于ACPSO优化SVR的棒材连轧轧制力预测研究[J].仪器仪表学报,2012,33(11):2579-2585.
[9]吴东升.钛合金棒材连轧过程的智能优化控制方法研究[D].长春:长春理工大学,2013.
[10]姚智胜,邵春福,高永亮.基于支持向量回归机的交通状态短时预测方法研究[J].北京交通大学学报,2006,30(3):19-22.
[11]李晓磊.一种新型的智能优化方法—人工鱼群算法[D].杭州:浙江大学,2003.
[12]王联国.人工鱼群算法及其应用研究[D].兰州:兰州理工大学,2009.
[13]姚卫红,方仁孝,张旭东.基于混合人工鱼群优化SVR的交通流量预测[J].大连理工大学学报,2015,55(6):1-6.
[2]孙一康.带钢冷连轧计算机控制[M].北京:冶金工业出版社,2002.
[3]马更生,彭文,邸洪双,等.带钢热连轧换规格轧制力自学习优化[J].东北大学学报:自然科学版,2015,36(12):1715-1718.
[4]刘俊,杨荃,何安瑞,等.贝叶斯框架下的LS-SVM在热连轧机轧制力预报中的应用[J].钢铁研究学报,2011,23(6):5-8.
[5]刘莉,李传峰,张广军.基于BP神经网络斜轧穿孔轧制力的预测[J].山东冶金,2013,35(2):43-44.
[6]薛涛,杜凤山,孙静娜,等.基于FEM-ANN的冷轧带钢轧制力预报[J].中南大学学报:自然科学版,2013,44(11):4456-4460.
[7]杨景明,孙晓娜,车海军,等.基于蚁群算法的神经网络冷连轧机轧制力预报[J].钢铁,2009,44(3):52-55.
[8]吴东升,王大志,杨青,等.基于ACPSO优化SVR的棒材连轧轧制力预测研究[J].仪器仪表学报,2012,33(11):2579-2585.
[9]吴东升.钛合金棒材连轧过程的智能优化控制方法研究[D].长春:长春理工大学,2013.
[10]姚智胜,邵春福,高永亮.基于支持向量回归机的交通状态短时预测方法研究[J].北京交通大学学报,2006,30(3):19-22.
[11]李晓磊.一种新型的智能优化方法—人工鱼群算法[D].杭州:浙江大学,2003.
[12]王联国.人工鱼群算法及其应用研究[D].兰州:兰州理工大学,2009.
[13]姚卫红,方仁孝,张旭东.基于混合人工鱼群优化SVR的交通流量预测[J].大连理工大学学报,2015,55(6):1-6.