近年来将BP网络模型应用到大气污染浓度预报中，并建立了大气污染物浓度的神经网络预报模型。
Recent years, BP model has been applied to atmospheric pollution forecast, a neural network prediction model of atmospheric pollutant concentration is set up.
将免疫算法与神经网络理论相结合，提出了免疫神经网络预报模型，以预报油库油气浓度。
A immune algorithm neural network model for predicting oil gas is presented by means of combining immune algorithm with neural network theory.
该模型首先用历史数据对网络进行训练，然后利用训练好的模型进行油气浓度的趋势预测，最后结合某油气预报实例检验了免疫神经网络模型的可行性。
First of all, the network is trained by history data, then the model is used to predict the general development trend of oil gas, finally the oil gas thickness is predicted.
在模型中既考虑了气象条件的作用，又考虑了污染排放量和起报日的污染浓度，与以往的空气污染预报统计模型相比，所依据的物理基础更可信一些。
In this model the effect of meteorological element, output and concentration of air pollutant were considered, so it's physical foundation may be more believable than pure statistics model.
在模型中既考虑了气象条件的作用，又考虑了污染排放量和起报日的污染浓度，与以往的空气污染预报统计模型相比，所依据的物理基础更可信一些。
In this model the effect of meteorological element, output and concentration of air pollutant were considered, so it's physical foundation may be more believable than pure statistics model.
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