| 基于RBF神经网络的黄河含沙量测量数据融合研究 |
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| 引用本文： | 刘明堂,张成才,田壮壮,刘雪梅,江恩惠,李黎.基于RBF神经网络的黄河含沙量测量数据融合研究[J].水利水电技术,2015,46(1):126-130. |
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| 作者姓名： | 刘明堂 张成才 田壮壮 刘雪梅 江恩惠 李黎 |
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| 作者单位： | （１.郑州大学水利与环境学院，河南郑州450001；2.华北水利水电大学信息工程学院，河南郑州450045；
3.黄河水利科学研究院水利部黄河泥沙重点实验室，河南郑州450003） |
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| 基金项目： | 2013年度水利部公益性行业科研专项项目，河南省高校科技创新团队支持计划资助，2012年度国家自然科学基金项目，水利部黄河泥沙重点实验室开放课题基金项目，华北水利水电大学创新实验项目资助 |
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| 摘 要： | 针对黄河含沙量高、泥沙时空分布不均和环境变化显著等特点,提出了采用电容式差压法来测量黄河含沙量,建立了基于RBF神经网络的含沙量测量的数学模型。将含沙量信息值与温度、深度和流速值作为RBF网络的输入,进行含沙量测量的反演和误差分析。试验结果表明,基于RBF的数据融合方法能够有效地消除环境影响,提高系统测量的精度和稳定性。
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| 关 键 词： | 含沙量测量 电容式差压法 数据融合 神经网络 |
| 收稿时间： | 2013-03-11 |
RBF neural networks based-study on data fusion for measurement of sediment concentration of Yellow River |
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| LIU Mingtang,ZHANG Chengcai,TIAN Zhuangzhuang,LIU Xuemei,JIANG Enhui,LI Li.RBF neural networks based-study on data fusion for measurement of sediment concentration of Yellow River[J].Water Resources and Hydropower Engineering,2015,46(1):126-130. |
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| Authors: | LIU Mingtang ZHANG Chengcai TIAN Zhuangzhuang LIU Xuemei JIANG Enhui LI Li |
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| Affiliation: | （1.School of Environment and Water Conservancy， Zhengzhou University， Zhengzhou450001， Henan, China;2.Department of
Information Engineering， North China University of Water Conservancy and Electric Power， Zhengzhou450045，Henan, China；
3.Key Laboratory of Yellow River Sediment Research of the Ministry of Water Resources，Yellow River Institute of Hydraulic
Research， Zhengzhou450003, Henan, China） |
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| Abstract: | In accordance with the characteristics of the Yellow River， such as high sediment concentration， uneven spatial and temporal distribution of sediment concentrations， significant environment change， etc.， it is proposed to measure the sediment concentration of the river with the of capacitive differential pressure， and then a RBF neural networks based-mathematical model for measuring the sediment concentration is established; for which the values of sediment concentration， water depth and velocity are taken as the input valued of the RBF networks for making the inversed and error analysis on the measurement of the sediment concentration. The experiment result shows that the RBF neural networks based-data fusion method can effectively eliminate the environmental impacts and then enhance both the accuracy and stability of the measurement. |
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| Keywords: | measurement of sediment concentration method of capacitive differential pressure data fusion neural network |
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