| 基于LS-SVM的螺旋定量加料预测及试验研究 |
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| 引用本文： | 崔守娟,张西良,徐云峰,粟强,孙祥,张宇.基于LS-SVM的螺旋定量加料预测及试验研究[J].排灌机械工程学报,2016,34(12):1099-1104. |
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| 作者姓名： | 崔守娟 张西良 徐云峰 粟强 孙祥 张宇 |
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| 作者单位： | 1.江苏大学机械工程学院, 江苏 镇江 212013; 2.江苏大学现代农业装备与技术教育部重点实验室, 江苏 镇江 212013 |
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| 摘 要： | 为提高螺旋定量加料性能,开展了智能预测技术对加料量准确性的影响研究.针对考虑螺旋速度和填充率等因素对螺旋不连续定量加料的影响难以用精确数学模型来描述加料量的问题,以螺旋加料装置为研究对象,加料量为预测目标,螺杆旋转角度为主影响因子,螺旋速度和填充率为影响因素, 建立了一种基于最小二乘支持向量机(LS-SVM)与旋转角度的加料量预测模型,研究加料量与主影响因子和影响因素之间的复杂非线性关系.采用交叉验证方法辨识模型参数,开展螺旋不连续定量加料量预测与实际加料试验.结果表明:该模型的预测结果与设定值较吻合,优于理论估算和BP神经网络预测模型,采用分料装置填充率接近1时,预测误差平均为±0.02.该模型可应用于螺旋不连续定量加料的预测与控制.
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| 关 键 词： | 螺旋加料机 模型 最小二乘支持向量机 加料量 旋转角度 |
| 收稿时间： | 2016-09-20 |
Experimental research and prediction of screw dosing based on least squares support vector machine |
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| CUI Shoujuan,ZHANG Xiliang,XU Yunfeng,SU Qiang,SUN Xiang,ZHANG Yu.Experimental research and prediction of screw dosing based on least squares support vector machine[J].Journal of Drainage and Irrigation Machinery Engineering,2016,34(12):1099-1104. |
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| Authors: | CUI Shoujuan ZHANG Xiliang XU Yunfeng SU Qiang SUN Xiang ZHANG Yu |
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| Institution: | 1.School of Mechanical Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China; 2.Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, Jiangsu University, Zhenjiang, Jiangsu 212013, China |
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| Abstract: | The feeding precision of screw feeder is determined by various factors, such as the rotational speed, filling rate and so on. It is usually difficult to calculate the accurate feeding quantity through a precise mathematical model. In this paper, we predicted the feeding quantity through machine learning based on three factors. The most important factor was the rotational angle, the other two factors were rotational speed and filling rate. The least squares support vector machine(LS-SVM)was used to learn the nonlinear relationship between the feeding quantity and these factors. To achieve the best performance, the parameters of LS-SVM were tuned through the cross-validation. A real feeding experiment was carried out to verify our method. The experimental results demonstrate that the prediction values of LS-SVM are consistent with the real values. Moreover, the precision of our LS-SVM is better than the estimation from theoretical calculation and BPNN. The prediction error of our method is only ±0.02 if the filling rate is close to 1. In summary, our method has high precision and can be applied to the control of screw feeder. |
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| Keywords: | screw feeder models least squares support vector machine(LS-SVM) feeding quantity rotational angle |
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