针对电容法测量河流含沙量过程中易受环境因素影响而导致测量结果不准确的问题,提出基于卡尔曼滤波和长短期记 忆神经网络(Kalman-LSTM)的融合模型。 先采用卡尔曼滤波器进行滤波处理,减小传感器测量的随机误差;再通过 LSTM 神经 网络模型对含沙量信息和环境量信息进行多传感器数据融合,减小环境因素对电容法测量含沙量的影响;最后建立了电容法测 量含沙量的 Kalman-LSTM 融合模型。 为了验证 Kalman-LSTM 融合模型的融合效果,与 BP 模型、RBF 模型和 LSTM 模型对比, 比较各模型的均方根误差、最大绝对误差、平均绝对误差和平均相对误差。 实验结果表明,Kalman-LSTM 融合模型的平均相对 误差为 2. 54%,均方根误差为 2. 47 kg / m 3 ,该融合模型能有效降低环境因素对含沙量测量的影响,提高电容法测量含沙量的准 确性。
In order to solve the problem of inaccurate measurement results due to environmental factors while measuring the sediment concentration of rivers with the capacitance method, a fusion model based on Kalman filtering and LSTM (Kalman-LSTM) is proposed. Firstly, the Kalman filtering is used for filtering to reduce the random error of sensor measurement. Then, the LSTM was used to integrate multi-sensor data of sediment content information and environmental content information, to reduce the influence of environmental factors on sediment content measurement by the capacitance method. Finally, a Kalman-LSTM fusion model for measuring sediment concentration by capacitance method was developed. To verify the fusion effect of the Kalman-LSTM fusion model, the root mean square error, maximum absolute error, average absolute error, and average relative error of each model are compared with the BP model, the RBF model, and the LSTM model. The experimental results show that the average relative error of the Kalman-LSTM fusion model is 2. 54% and the root mean square error is 2. 47 kg / m 3 . The fusion model can effectively reduce the influence of environmental factors on sediment concentration measurement and improve the accuracy of sediment concentration measurement by the capacitance method.
邓罗晟,车国霖,金建辉.基于 Kalman-LSTM 模型的悬浮质含沙量测量[J].电子测量与仪器学报,2023,37(5):163-170复制