矿井瓦斯涌出量

矿井瓦斯涌出量灰色GM（1，1）预测模型的分析

Analysis of grey GM ( 1 , 1 ) prediction model of mine gas emission rate

该预测模型为矿井瓦斯涌出量时间动态数列的预测提供了一条新的途径。

The forecasting model provides a new way to the tiem dynamic numerical array of mine gas emission rate .

基于GM（1,1）模型的矿井瓦斯涌出量预测研究

Study of Predicting the Outflow of Mine Gas Based on GM ( 1,1 ) Model

通过对矿井瓦斯涌出量时间序列的模糊分形处理，用BP神经网络对影响因素间的非线性关系进行拟合。

After the time series fuzzy fractal processing of the mine gas emission quantity , the non linear relations of the influence factors were combined with BP neural network .

在分析了传统的矿井瓦斯涌出量预测方法的基础上，应用灰色系统理论GM（1,1）模型，对矿井瓦斯涌出量进行预测，并在实际中得到了较好的验证。

Based on the analysis of the traditional methods for predicting the amount of gas gushed from mine , Model GM ( 1,1 ) of the gray theory is applied for this prediction , and is well rectified in practice .

为解决现有的矿井瓦斯涌出量预测方法不能实现自动化处理等问题，本文开发了基于神经网络的矿井瓦斯涌出量预测系统GGP，并介绍了该系统在实际工程上的应用情况。

In order to solve the problem that automatic settlement can 't be realized with existing predicting methods for mine gas emission , the authors have developed the Neural Network-based GGP Mine Gas Emission Predicting System and introduced the application of the system in practical engineering .

矿井瓦斯涌出量时间序列的分形特性分析

Fractal feature analysis of time series of gas emission in coal mine

分源预测法在新建矿井瓦斯涌出量预测中的应用

Application of Forecast from Different Sources in New Mine Gas Emission Forecast

基于灰色理论的矿井瓦斯涌出量预测模型研究

Study on Prediction Model of Mine Gas Emission Based on Grey Theory

煤、油、气共存矿井瓦斯涌出量主控因素的确定

Main controlled factors of gas emission quantity in oil-gas mine

基于神经网络的矿井瓦斯涌出量预测系统及应用

Neural Network-based Mine Gas Emission Predicting System and Its Application

R/S分析及矿井瓦斯涌出量的分形预测

R / S analysis and fractal prediction of gas emission in coal mine

矿井瓦斯涌出量的灰色小波神经网络预测模型

Forecasting Method of Grey Theory and Wavelet Neural Network for Mine Gas Gushing

瓦斯地质数学模型法预测矿井瓦斯涌出量研究

Study on mathematical model of coalbed gas geology used to prediction of mine gas emission

用灰色建模法预测矿井瓦斯涌出量

Predicting the Amount of Gas Gushed from Mine by Model GM ( 1,1 ) of Gray System Theory

利用蒙特卡洛统计分析法研究了矿井瓦斯涌出量以及通风网络瓦斯流的模型，并给出了具体的实现步骤，简单论述如下。

The thesis studies the model and the specific approach of the gas emission and the gas flow .

介绍了数字分析在矿井瓦斯涌出量预测中的应用。

This paper describes the application of numerical analysis in gas emission prediction in virgin zone of a mine .

通过部分采掘实践检验，证明矿井瓦斯涌出量和突出危险性区域预测的可信度较高。

Its reliability is upper that the forecast of mine gas emission and outburst fatalness through verified by fractional excavating practice .

论述了分源预测法原理，并通过实例计算介绍了该方法在新建矿井瓦斯涌出量预测中的应用。

This paper discusses the principles of forecast from different sources , and introduces the application in the new mine gas emission forecast through examples of calculation .

通风参数计算的灰色建模法用灰色建模法预测矿井瓦斯涌出量

THE GREY MODELS USED FOR THE CALCULATION OF VENTILATION VARIABLES Predicting the Amount of Gas Gushed from Mine by Model GM ( 1,1 ) of Gray System Theory

将模糊控制技术、分形理论中的时间序列分析方法与神经网络技术有机地结合起来，并运用于矿井瓦斯涌出量的预测中。

The paper is to organically combine the time series analysis method and neural network technology in the fuzzy control technology and fractal theory to predict mine gas emission quantity .

实例表明，该模型的计算精度符合工程实际，可用于矿井瓦斯涌出量的预测。

The prediction example Shows that the precision of the modified model can meet the necessity of practical engineering , so can be applied to predict the outflow of mine methane .

长期以来，我国一直沿用传统的矿山统计法和瓦斯含量法来预测矿井瓦斯涌出量，这两种预测方法都有各自的适用条件和局限性。

The methods based on the old statistics and firedamp content have been used to predict the gas emission in China , yet it was unsuitable and inaccurate in some special conditions .

由于矿井瓦斯涌出量的影响因素、关联变量和约束条件复杂，对各因素之间的影响程度难以区分和把握，因此在预测预报中的精度较差。

Because the influential factors and relation variables and constraining conditions is complex , and it is very difficult to differentiate and deal with the influence degree , the precision of its predicting and forecast is more difference .

新建矿井的瓦斯涌出量预测地质方法及其应用

Application of Gas Emission Geological Prediction Method of New Mine

灰色理论在矿井绝对瓦斯涌出量预测中的应用

The Application of Grey Theory to Forecast of Absolute Volume of Mine Feeder Gas

煤层瓦斯含量是煤层瓦斯主要参数之一，是矿井进行瓦斯涌出量预测和煤与瓦斯突出预测的重要基础参数。

Coal seam gas content is one of the main parameters of coal seam gas , is the mine gas emission prediction and important basic parameters of the coal and gas outburst prediction .

利用该预测模型对一些矿井进行瓦斯涌出量大小的预测研究表明，该模型具有较强的适用性和较高的准确性，用其对动态系统的分析更为适合。

It has shown in practice that this forecasting model using for predicting mine gas emission rate , has greater adaptability and higher accuracy , and is much better suitable to analysis of dynamic system .

再次分别利用一元回归和多元回归分析的方法，研究了有关因索与煤层瓦斯含量的关系，并建立了煤层瓦斯含量预测模型；运用矿山统计法，对矿井的相对瓦斯涌出量进行了预测。

Thirdly , the relationship between the factors involved and coal seam gas content were studied by using single and multiple regression analysis and constructed its forecast model ; forecasted the relative gas emission of the mine with the method of mine statistics .

基于BP算法的矿井掘进工作面瓦斯涌出量预测

Prediction of Gas Amount Emitted from Mine Heading Face Based on Back-Propagation Algorithm