Prediction of Gas Emission Based on Information Fusion and Chaotic Time Series

1 Introduction Gas emission has a great effect on the produc- tion of a coal mine. It is also a major reason for the installation or expansion of ventilation in coal mines. The accuracy of gas emission prediction is very im- portant to the security of the production of the mine and the improvement of economic effectiveness. Different mines and working faces have differ- ent rules concerning gas emission and gas prediction models should be changed when exterior conditions change. There are man…

Prediction of Gas Emission Based on Information Fusion and Chaotic Time Series

In order to make more exact predictions of gas emissions, information fusion and chaos time series are combined to predict the amount of gas emission in pits. First, a multi-sensor information fusion frame is established. The frame includes a data level, a character level and a decision level. Functions at every level are interpreted in detail in this paper. Then, the process of information fusion for gas emission is introduced. On the basis of those data processed at the data and character levels, the chaos time series and neural network are combined to predict the amount of gas emission at the decision level. The weights of the neural network are gained by training not by manual setting, in order to avoid subjectivity introduced by human intervention. Finally, the experimental results were analyzed in Matlab 6.0 and prove that the method is more accurate in the prediction of the amount of gas emission than the traditional method.