摘要
基于RBF神经网络的SO2的浓度预测,选取一定的历史数据建立径向基函数神经网络训练模型,进行拟合训练。将芜湖市1993年到2001年大气SO2的浓度历史数据用于径向基函数神经网络,建立训练网络模型,通过训练优化提高训练可靠性。再用该模型对芜湖市大气中SO2的浓度进行预测。
For the prediction of SO2 density based on the radial base function （RBF） neural network, according to the historical data of SO2 density, RBF neural network model is established to fit training. The historical data of SO2 density in atmosphere from 1993 to 2001 of Wuhu city was applied to RBF neural network, the network model for training was set up, and the model was trained and optimized to improve the dependability of the training. The density of SO2 in atmosphere of Wuhu city was predicted with the model.
出处
《兵工自动化》
2005年第5期67-68,共2页
Ordnance Industry Automation
关键词
径向基函数
神经网络
二氧化硫
浓度预测
Radial base function （RBF）
Neural network
SO2
Density prediction
