径向基函数人工神经网络预测污水处理厂出水水质

根据人工神经网络的理论和方法，建立了径向基函数神经网络模型．用镇江市征润州污水处理厂的实测数据进行模型训练和预测水质验证，采用最近邻聚类学习算法选取聚类中心，表明模型有较强非线性处理能力和逼近能力，并具有学习时间短，网络运算速度快，性能稳定等优点．通过模型预测结果和实测值的比较，发现用径向基函数神经网络模型预测污水出水水质，具有预测精度高，使用方便，适应性强等优点，因此可望将其用于污水厂出水水质的预测．

Predication of wastewater treatment plant effluent quality using radial basis function neural network

The Radial Basis Function Neural Network model for predicting wastewater treatment plant effluent quality is established based on the theory and methodology of neural network. The data obtained from wastewater treatment plant were used to train and verify the model. The model demonstrates its capability to approach function and treat non--linear problem by using nearest neighbor cluster algorithm to select the clustering center. The main advantage of the RBF based model is its accuracy, time saving, fast running and stability behavior. The good agreement between predicted and measured data was observed. Because of these benefits, it is believed that the model could find application in predicting wastewater treatment plant effluent quality.