计算机工程与应用Issue(14)：1-4,25,5.DOI:10.3778/j.issn.1002-8331.1403-0051
合理遗忘选择训练样本的煤矿瓦斯涌出量预测
Coal gas emission prediction selective forgetting training samples
摘要
Abstract
In order to improve the prediction accuracy of coal gas emission, a novel prediction model of coal gas emis-sion is proposed based on selective forgotten training samples to solve problem of training sample selection. Firstly, the forgetting factor is introduced to weaken the old training samples and highlight the role of new data simultaneously, and training samples are updated reasonably, and then the least squares support vector machine is used to establish the predic-tion model of gas emission, and finally, the simulation analysis is carried out to test the performance of model. The results show that the proposed model improves the modeling efficiency of coal gas emission and can obtain good coal gas emis-sion prediction results.关键词
煤矿瓦斯涌出量/最小二乘支持向量机/仿真实验/预测精度Key words
coal gas emission/least squares support vector machine/simulation experiment/prediction precision分类
信息技术与安全科学引用本文复制引用
高明明,邵良杉..合理遗忘选择训练样本的煤矿瓦斯涌出量预测[J].计算机工程与应用,2014,(14):1-4,25,5.基金项目
国家自然科学基金（No.70971059）；中国煤炭工业协会科学技术研究指导性计划资助项目（No.MTKJ2011-339）。 （）
