THE GENETIC NEURAL NETWORKS AND GAS CONTENT FORECAST
Wu Cai · Dixue qianyuan · 2003
Forecasting the gas content depends on an establishment of a non liner functional relation of many factors; the accuracy of the forecasting model for the gas content is determined by the peculiarities of the interaction and coupling between all the affecting factors. This paper combines neural networks with genetic algorithm; on the basis of NN theory, and applying GA to optimize the construction and the power size of NN, a forecasting model of gas content is established. On the basis of the data in laboratory, the training and testing samples of GA NN model are founded, including 38 typical specimens. The verifying outcome has been compared with the output of back assay model, normal BP NN, and auto adapting NN. The result shows that the GA NN model is reliable and precise, which founds the basis for promoting the integration of soft calculation and gas geology.