基于PCA和RBF神经网络的绞吸挖泥船实时产量预测

绞吸挖泥船在实际作业过程中的动态特性非常复杂,影响产量的控制因素众多。若这些控制因素全部参与产量预测比较耗时。为了实时训练网络及预测产量,先对影响绞吸挖泥船产量的控制因素进行主成分分析(PCA),再根据分析结果约减控制因素;在系统仿真建模中,分别以全部因素和约减后因素作为径向基(RBF)神经网络的输入变量,以产量作为输出变量来建立绞吸挖泥船产量预测模型。结果表明,减少输入变量,不仅降低产量预测模型的复杂程度,减少神经网络计算耗时,而且能保持模型良好的预测精度,从而为施工现场的操作人员提供实时的产量参考。

Real-time production prediction of CSD based on PCA and RBF neural network

In the actual operation process,the dynamic characteristics of the cutter suction dredger( CSD)are very complicated,and many factors affect the production. It will consume much time if all these control factors participate in the output prediction. To train the network and predict the production in real-time,the PCA is utilized to these control factors. Then,the number of control factors is reduced according to the results. In the system simulation modeling,the whole control factors and reduced control factors are used separately as the input variables of the RBF neural network,and the production is used as the output variable to establish the prediction model of CSD’ s production. The results show that the prediction model of production can be simplified and the computation time can be reduced while the input variables are reduced,and the prediction accuracy of the model can be maintained,providing real-time production reference for operators on the construction site.