基于RBF神经网络的水体污染物含量建模与预测

Levels of Pollutants in Water Modeling and Forecasting Based on RBF Neural Network

对水体中污染物含量进行预测具有十分重要的意义。将时间序列作为RBF神经网络的输入,对水体污染物含量的预测做了建模研究。实验结果表明,RBF神经网络的输出值与实际值之间的误差在可以接受的范围,因此在实际应用中,可以将RBF网络方法作为一种考虑采用的方法。

Forecasting the levels of pollutants in water is greatly significant. The time series is taken as the input of RBF neural network, and a lot of research has been done on the forecasting the levels of pollutants in water. The experimental result indicates that the error between the output of the RBF neural network and the actual numerical values is in the acceptable range. As a result, the RBF network can be considered as an adoptable method in practice.