摘要
本文将偏最小二乘回归与神经网络耦合,建立了泉流量预报模型。利用偏最小二乘法对影响岩溶泉流量的诸多因素进行分析,提取对因变量影响强的成分,从而克服了变量之间的多重相关性问题,降低了神经网络的输入维数。同时,利用神经网络建模可以较好地解决非线性问题。实例表明,本耦合模型的拟合和预报精度均优于独立使用偏最小二乘回归或神经网络建模的精度。
A model for predicting karst spring flow based on the combination of neural network and partial least square method is proposed. The factors affecting the spring discharge are analyzed by means of partial least square method to extract the most important components so that not only the problem of multi-correlation among variables can be solves but also the amount of input dimensions of the neural network can be reduced. Besides, the application of neural network helps to solve the problem of non-linearity of the model. The application example shows that the proposed model has higher precision than those models based on neural network method or partial least square method only.
出处
《水利学报》
EI
CSCD
北大核心
2004年第9期68-72,共5页
Journal of Hydraulic Engineering
