基于免疫神经网络模型的油气浓度预测研究

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。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。结果表明，该智能预报模型能够较好地识别油气扩散的变化规律，预报精度明显高于神经网络模型。该结论拓宽了免疫神经网络模型的应用范围，为油库油气浓度的科学预报提供了一种新方法。

Study on Trend Estimate of the Oil Gas Based on the Immune Algorithm Neural Network

A immune algorithm neural network model for predicting oil gas is presented by means of combining immune algorithm with neural network theory . First of all,the network is trained by history data,then using the model is set up to predict the general development trend of the oil gas,finally the oil gas is predicted. Results show that the immune algorithm neural network model can predict variety rule of oil gas better and has higher accuracy than that of neural network. It enlarges the applicalion scope of immune algorithm neural network model,and provides a new scientific method for predicting oil gas.