Gas Outburst Risk Analysis Based on Pattern Recognition of RSSVM Model

Coal and gas outburst disasters are usually accompanied by some of the characteristics of events, through the analysis of gas accident monitoring data, we can draw the pattern characteristics of a gas accident, which can highlight the situation in the future identification of gas according to features of the database, support vector machines in a small sample, high-dimensional pattern recognition has shown great advantages, the combination of VC dimension theory and structural risk minimization principle, the limited sample modal learning experience, can effectively achieve the effect of classification and pattern recognition, In combination with rough set theory, the original sample data reduction, support vector machine so as to post the data to facilitate processing and pattern identification, and finally validated through case study and practical validity of the model.