Abstract: Due to the fact that it is difficult to measure the chemical oxygen demand (COD) of sewage water environment parameters, a kind of gray level theoretical prediction model based on radial basis function neural network (GM-RBF) is proposed. The proposed GM-RBF can predict the chemical oxygen demand. The gray theory is used to predict the development and change of the system behavior, and the precision of the prediction model can be improved by combining the high precision approximation ability of the radial basis function neural network. The modeling and prediction of the key water quality parameters in the process of wastewater treatment is studied. The results show that the model can predict the COD with high accuracy, and the prediction is close to the actual value.

Key words: Chemical oxygen demand, Gray prediction, RBF neural network, Soft measurement

摘要： 针对测量污水环境水参数化学需氧量(COD)难于测量的问题，提出了基于径向基网络的灰度理论预测模型（GM-RBF），对化学需氧量进行预测。利用灰度理论能对系统行为的发展变化进行预测的特点，结合径向基神经网络的高精度逼近能力，提高了预测模型的精度。研究了对污水处理过程关键水质参数的建模预测，实验证明该模型能以较高精度对COD进行预测，预测值最接近真实值，提供了可靠的COD参数值。

关键词: 化学需氧量, 灰度预测, RBF神经网络, 软测量