| 基于影响因素的SVR备件需求预测研究 |
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| 引用本文： | 周伟,白占胜.基于影响因素的SVR备件需求预测研究[J].中国修船,2009,22(3):40-42. |
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| 作者姓名： | 周伟 白占胜 |
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| 作者单位： | 海军工程大学,船舶与动力学院,湖北,武汉,430033 |
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| 摘 要： | 文章介绍了支持向量机学习算法，说明其特点，并引出基于影响因素的支持向量回归的备件需求预测方法，用某型备件的历史需求数据例证此法的可行性与精确度。
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| 关 键 词： | 支持向量机 备件需求预测 影响因素 |
Research on demand-forecasting for spare parts of Support Vector Regression(SVR)based on impact factors |
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| ZHOU Wei,BAI Zban-sheng.Research on demand-forecasting for spare parts of Support Vector Regression(SVR)based on impact factors[J].China Shiprepair,2009,22(3):40-42. |
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| Authors: | ZHOU Wei BAI Zban-sheng |
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| Abstract: | This paper introduces the algorithm of machine learning of Support Vector Machines （SVM） and illuminates its characters, educing the forecasting method for spare parts demanded by Support Vector Regressin （SVR） based on impact factors. By analyzing the former-demand data of some type spare parts, we demonstrate the feasibility and precision of this method. |
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| Keywords: | Support Vector Machines （SVM） forecasting for spare parts demand impact factors |
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