| 基于支持向量机的起重机电机振动特征参数预测 |
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| 引用本文： | 胡雄,王志欣.基于支持向量机的起重机电机振动特征参数预测[J].上海海事大学学报,2006,27(4):18-21. |
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| 作者姓名： | 胡雄 王志欣 |
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| 作者单位： | 1. 上海海事大学,物流工程学院,上海,200135
2. 上海交通大学,机械动力学院,上海,200030 |
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| 基金项目： | 上海市教委科研项目(2004095) |
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| 摘 要： | 提出用支持向量机方法对岸桥电机运行性能特征参数进行预测的方法,并构造基于支持向量回归的单步及多步预测模型.利用该模型对岸桥电机振动烈度时间序列分别进行1步和4步预测,并与自回归(AR)模型的预测值进行比较.实验结果表明,基于支持向量机预测模型的1步预测和4步预测精度均比AR模型的预测精度高,因此该模型对岸桥电机的运行性能特征参数具有较好的短期和长期预测能力.
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| 关 键 词： | 预测 支持向量机 集装箱起重机 AR模型 |
| 文章编号： | 1672-9498（2006）04-0018-04 |
| 收稿时间： | 2006-08-15 |
| 修稿时间： | 2006-10-26 |
Vibration feature forecasting on crane motor using support vector machine |
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| HU Xiong,WANG Zhixin.Vibration feature forecasting on crane motor using support vector machine[J].Journal of Shanghai Maritime University,2006,27(4):18-21. |
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| Authors: | HU Xiong WANG Zhixin |
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| Institution: | 1. Logistics Eng. College, Shanghai Maritime Univ. , Shanghai 200135, China; 2. School of Mechanical Eng., Shanghai Jiaotong Univ., Shanghai 200030, China |
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| Abstract: | A feature forecasting method based on support vector machine(SVM) is proposed,and its one-step and multi-step forecasting models are developed.The one-step and four-step forecastings of the motor vibration severities time serial are made based on the models,and the forecasting results are compared with those using AR model.The precisions of one-step and four-step forecastings are higher than those using AR model,which shows that the SVM model has the better performances in short and long-term forecastings. |
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| Keywords: | forecasting support vector machine container crane AR model |
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