模糊支持向量机在煤与瓦斯突出预测中的研究  

Research on coal and gas outburst prediction using fuzzy support vector machines

作　　者：杨力[1] 耿纪超[1] 汪克亮[1] 

机构地区：[1]安徽理工大学经济与管理学院,安徽淮南232001

出　　处：《中国安全生产科学技术》2014年第4期103-108,共6页Journal of Safety Science and Technology

基　　金：国家自然科学基金资助项目(71071003)(71371014);安徽省高效省级自然科学研究项目(KJ2010B323)

摘　　要：模糊支持向量机(FSVM)综合了模糊理论和支持向量机(SVM)的学习理论,不仅继承了SVM在小样本情况下所具有的较强识别能力的特点,并且比SVM拥有更好的学习能力。在FSVM算法中,每个样本被赋予一个隶属度值,使得构造目标函数时不同的样本有不同的贡献,达到最大限度的消除噪声或者孤立点的效果。运用了灰色关联分析(GRA)对煤与瓦斯突出指标进行提取,引入了一个合适的模糊隶属度函数,并在此基础上提出了基于FSVM的煤与瓦斯突出预测的模型,通过实际数据的验证和其他预测方法的对比,证明了FSVM模型能够满足煤与瓦斯突出预测的要求。最后,将FSVM和传统SVM对同一组数据进行训练,证明了FSVM相比较传统SVM拥有更高的精确度。Fuzzy support vector machine （FSVM）, which integrates the learning theory of support vector machine （ SVM） and fuzzy set theory , not only inherits the strong recognition ability on small samples of SVM , but also has better generalization ability than SVM .In FSVM algorithm , each sample is given a membership value in order that different samples in objective function have different contribution .So, FSVM can maximum eliminate influence of noise or isolated point.In this paper, grey relation analysis method （GRA） was applied to select coal and gas out-burst features .A fuzzy membership was introduced to each input point and a gas outburst prediction model was established based on FSVM .Through validation of practical data and comparison with other prediction methods , it showed that the FSVM model has good performance of coal and gas outburst prediction .In conclusion , the performance of FSVM was compared with that of SVM and it showed that FSVM performs better than SVM .

关 键 词：模糊支持向量机 支持向量机 灰色关联分析法 煤与瓦斯突出 

分 类 号：X936[环境科学与工程—安全科学]