Abstract
ABSTRACTIn view of the local extreme problem of the gradient descent algorithm, which makes the working face of mine gas emission prediction uncertainly, this paper combined Wolf pack algorithm (WPA) with complex neural network nonlinear prediction method to the established new prediction model. The WPA shows good global convergence and computational robustness in the solving process of complex high-dimensional functions. Working face in a coal mine as a case, this paper selects seven factors as input variables of the mine gas emission prediction, uses training data to mature prediction model and adopted it to predict six group gas emission data. Research results show that the mean absolute percentage value of the complex neural network model which has been optimized by WPA is 0.06%, the root mean square error value is 0.0191, the mean absolute error value is 0.0175 and the equal coefficient value is 0.9979. The prediction results are very close to the real value, and the change trend is highly consistent with the actual situation.
Highlights
With the acceleration of coal mining, the mining area and mining depth gradually increase, and the gas emission problem is prominent
With the development of computer technology and data mining technology, many prediction methods are widely used in gas emission prediction, including neural network, fuzzy theory and support vector machine (SVM), and they have stronger nonlinear computing power
Wolf pack algorithm (WPA) has been introduced into the prediction model of complex neural network, and the weights and parameters of the network are optimized
Summary
With the acceleration of coal mining, the mining area and mining depth gradually increase, and the gas emission problem is prominent. With the development of computer technology and data mining technology, many prediction methods are widely used in gas emission prediction, including neural network, fuzzy theory and support vector machine (SVM), and they have stronger nonlinear computing power. Some scholars take a fuzzy fractal process of mine gas emission time series, using backpropagation neural network of nonlinear relationship between the influencing factors of fitting (Zhang & Qiu, 2006), and fuzzy time series model is set up. The method of fuzzy data mining is used to process the gas monitoring data to realize the prediction of gas emission (Xu, Wang, & Wang, 2004) These studies have achieved good practical application effect, and have promoted the technical research of mine ventilation system optimization and gas disaster prediction and control.
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