引黄灌渠斗口流量软测量技术

引黄灌渠斗口水流量通常依据闸门开度、闸前和闸后水位等可观测信息估算。自动测量装置具有“软仪表”的典型特征,建立精确适用的软测量模型十分关键。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,建立了基于BP网络和RBF网络的引黄灌渠斗口流量软测量模型,并精选水工试验数据构成训练样本集进行仿真训练。检验表明,基于人工神经网络的软测量模型输出值与期望值(标准三角量水堰的测量结果)吻合良好,斗口水流量软测量精度有显著改善。

Soft-sensing for Water Discharge at the Outlet of Irrigation Channel in the Yellow River

Water discharge at the outlet of irrigation channel in the Yellow River is estimated through measurable information such as open-level of floodgate,water levels at front and back of the floodgate and so on.The auto-measuring device has typical characteristics of soft-meter,therefore,it is most important to establish an exact model.Because trained ANN 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.The test shows that outputs of the ANN-based models approach to expected value(result from standard triangular weir) well.The precision of soft-sensing for discharge at the outlet of irrigation channel is improved remarkably.