| 引黄灌渠斗口流量神经元网络软测量模型 |
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| 引用本文： | 卢胜利,曹家麟,雷崇民,王福平,祝玲,吴学军.引黄灌渠斗口流量神经元网络软测量模型[J].计算机工程,2005,31(13):16-18,37. |
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| 作者姓名： | 卢胜利 曹家麟 雷崇民 王福平 祝玲 吴学军 |
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| 作者单位： | 上海大学机电工程与自动化学院,上海,200072;西北第二民族学院,银川,750021 |
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| 基金项目： | 国家自然科学基金资助项目“引黄灌渠斗口流量自动测量方法及装置研究”(60165001) |
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| 摘 要： | 鉴于训练后的人工神经网络可以精确逼近任意非线性函数，建立了引黄灌渠斗口流量BP网络和RBF网络软测量模型，精选水工试验数据构成训练样本集，在MATLAB6．5环境下进行了仿真训练。检验表明，基于人工神经网络的软测量模型的输出值与标准三角量水堰的测量结果(期望值)吻合情况良好，水流量测量精度大为改善。
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| 关 键 词： | 引黄灌渠 斗口流量 软测量 ANN |
| 文章编号： | 1000-3428(2005)13-0016-03 |
ANN-based Soft-sensing Model of Water-flow at Outlet of Irrigation Channel in Yellow River |
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| LU Shengli,Cao Jialin,Lei Chongmin,Wang Fuping,ZHU Ling,Wu Xuejun.ANN-based Soft-sensing Model of Water-flow at Outlet of Irrigation Channel in Yellow River[J].Computer Engineering,2005,31(13):16-18,37. |
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| Authors: | LU Shengli Cao Jialin Lei Chongmin Wang Fuping ZHU Ling Wu Xuejun |
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| Affiliation: | LU Shengli1,CAO Jialin1,LEI Chongmin2,WANG Fuping2,ZHU Ling2,WU Xuejun2 |
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| Abstract: | Because ANN trained can exactly approach to any nonlinear function, ANN-based soft-sensing models (BP and RBF) are established and trained by sample-set selected from experiment-data under MATLAB6.5. The examination shows that output of the ANN-based models approaches to result from standard triangular weir (expectation) well. The precision of water-flow measurement is improved largely. |
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| Keywords: | Irrigation channel in Yellow River Water-flow at the outlet Soft-sensing ANN |
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