基于粗糙集理论的瓦斯灾害信息特征提取技术

The technique of gas disaster information feature extraction based on rough set theory

为了准确预测煤与瓦斯突出的危险性,建立有效的煤矿瓦斯预警支持系统,针对煤矿瓦斯灾害的特点,本研究提出了一种新颖的基于粗糙集的瓦斯灾害特征提取算法。该算法首先利用维数化简技术对瓦斯灾害信息矩阵进行优化,并在此基础上,利用信息论中熵的概念和最大熵原理构建瓦斯灾害信息特征提取模型。通过实际应用,证实了粗糙集理论在瓦斯灾害特征提取与瓦斯灾害预测中的有效性和实用性。

In order to accurately predict coal and gas outburst danger and to establish an effective early-warming support system of gas in coal mine, a high efficient gas disaster feature extraction algorithm based on rough set was proposed in view of the characteristics of coal mine gas disaster. The algorithm first refined the gas disaster information matrix by using dimensionality reduction, then the entropy and max entropy in the concept of rough set theory were used to estab- lish data mining model of gas disaster prediction. The effectiveness and practicality of rough set theory in the prediction of gas disaster and feature extraction was confirmed through practical application.