【摘要】 为消除泥沙粒径和泥沙亮度在红外线法测量含沙量中的干扰,建立可用于红外泥沙测量的数学模型,采用BP人工神经网络方法,将红外泥沙测量传感器输出的散射光强度值、泥沙粒径因子和泥沙亮度值作为网络的输入,对应的泥沙含量值作为网络输出,通过对网络进行训练建立测量泥沙含量的人工神经网络模型。结果表明:神经网络模型对四种不同粒度泥沙样本含沙量的测量误差均小于20.0%,当含沙量在150～275 kg/m~3时,测量误差在8.0%以内。该方法可以有效消除泥沙粒径因子和泥沙亮度因子的影响,显著提高含沙量测量结果的准确性。更多还原

【Abstract】 In order to eliminate the interference of sediment particle size and brightness in measuring sediment concentration by infrared method, a mathematical model was established.Using BP artificial neural network algorithm, the scattered light intensity, sediment particle size factor and sediment brightness value output by infrared sediment measurement sensors are taken as the input of the network, and the corresponding sediment content value is taken as output of the network. The ANN model for measuring sediment content is established by training the network.The result shows that the measurement error of the four sediment samples with different particle sizes is less than 20.0% by using the model, and when the sediment content is between 150～275 kg/m~3, the measurement error is within 8.0%.This method can effectively eliminate the influence of the particle size factor and the brightness value of the sediment and improve the accuracy of the measurement results obviously.更多还原