| 基于支持向量机的水流挟沙力预测研究 |
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| 引用本文： | 熊建秋,李祚泳.基于支持向量机的水流挟沙力预测研究[J].水利学报,2005,36(10):1171-1175. |
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| 作者姓名： | 熊建秋 李祚泳 |
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| 作者单位： | 1. 四川大学,水利水电学院,四川,成都,610065
2. 成都信息工程学院,四川,成都,610041 |
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| 基金项目： | 973国家重点基础研究发展规划资助项目（2002CB412301）. |
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| 摘 要： | 本文阐述了支持向量机（SVM）的基本原理及特性，提出了基于SVM的水流挟沙力研究方法，并对30组高、中、低含沙量的水槽试验资料进行训练，训练值与实测值符合较好，再用训练好的SVM模型对4组试验数据进行了预测，预测结果与实测值相差较小。理论分析和实例结果验证了基于SVM的水流挟沙力研究方法比BP神经网络法具有更高的预测精度和可靠性。
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| 关 键 词： | 支持向量机 挟沙力 预测 BP神经网络 |
| 文章编号： | 0559-9350（2005）10-1171-05 |
| 收稿时间： | 2005-07-26 |
| 修稿时间： | 2005年7月26日 |
Sediment-carrying capacity forecasting based on support vector machine |
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| XIONG Jian-qiu,LI Zuo-yong.Sediment-carrying capacity forecasting based on support vector machine[J].Journal of Hydraulic Engineering,2005,36(10):1171-1175. |
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| Authors: | XIONG Jian-qiu LI Zuo-yong |
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| Affiliation: | 1. Sichuan University, Chengdu 610065, China ; 2. Chengdu University of Information Technology, Chengdu 610041, China |
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| Abstract: | The principle of support vector machine(SVM) is introduced and the method for forecasting sediment-carrying capacity based on this principle is proposed. The model is trained by 30 sets of experimental data with various suspension concentrations and applied to forecast another 4 sets of experimental data. The forecast is in good agreement with the measurement results. The comparison indicates that the accuracy and reliability of the proposed method are better than that of BP neural network method. |
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| Keywords: | support vector machine sediment-carrying capacity forecasting BP neural network method |
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