悬移质含沙量检测的Kalman-GORBF多源数据最优融合模型研究

Research on Optimal Model of Multi-source Data Fusion for Suspended Sediment Concentration Detection Based on Kalman-GORBF

针对悬移质含沙量在线检测易受环境因素的影响,提出基于卡尔曼(Kalman)滤波和遗传算法优化的径向基函数(Genetic Optimized Radial Basis Function,GORBF)神经网络的多源数据最优融合模型.首先简述Kalman滤波的基本原理,将基于音频共振法传感器的信号进行Kalman滤波;对悬移质含沙量、水温、电导率以及深度信息进行综合分析,找出对悬移质含沙量检测有关联的环境因素;并应用RBF神经网络对悬移质含沙量数据进行多源数据处理,融合了影响悬移质含沙量检测的环境因素;最后探讨遗传算法(Genetic Algorithm)优化径向基神经网络(RBF Neural Network)参数的方法,提出遗传算法来优化RBF的半径和离散尺度,获得了悬移质含沙量检测的多源数据最优融合效果,有效地减少环境因素对悬移质含沙量检测的影响.为了比较Kalman-GORBF多源数据融合模型的处理效果,在相同环境下还进行了Kalman-RBF,GORBF、多元线性回归和一元线性回归方法的处理,并进行悬移质含沙量测量的误差分析.试验结果表明,基于Kalman-GORBF多源数据最优融合的悬移质含沙量检测模型能够有效地消除环境影响,提高了悬移质含沙量检测的精度和稳定性.

To solve the problem that On-line detection of suspended sediment concentration is easily affected by environmental factors,a optimal model of multi-source data fusion,based on Kalman filter and genetic optimized Radial Basis Function(GORBF)neural network,is proposed.Firstly,the basic principle of Kalman filtering was briefly introduced,and the Kalman method was applied to filter the signal based on the audio resonance sensor.A comprehensive analysis of the effect of water temperature,conductivity,and depth on the detection of suspended sediment concentration was carried out and the factors that affect the detection of suspended sediment content were found out.The RBF neural network was used to process the multi-source data of suspended sediment concentration data,and the influencing factors of the environmental information during the on-line detection of suspended sediment concentration were fused.Finally,the genetic algorithm was used to optimize the gradient descent method of RBF neural network.The genetic algorithm was used to optimize the radius and discrete scale of RBF.The multi-source data of suspended sediment concentration detection was obtained.The optimal fusion reduced the influence of environmental factors effectively on the detection of suspended sediment concentration.In order to compare the processing effects of the multi-source data fusion model based on Kalman-GORBF,Kalman-RBF,GORBF,multivariate linear regression analysis and one-dimensional linear regression analysis were performed in the same environment,and comparative analysis of the detection errors of suspended sediment in various methods were also performed.The experimental results show that the suspended sediment concentration detection model,based on Kalman-GORBF multi-source data fusion,can effectively eliminate the environmental impact and improve the accuracy and stability of suspended sediment concentration detection.