摘要
基于模糊信息粒化对原始数据进行处理,利用支持向量机(SVM)以及差分进化算法(DE)建立预测模型,根据马尔科夫链进行残差修正,预测瓦斯浓度变化趋势。通过实验表明,该方法相较于直接对粒化数据进行SVM预测有了较大的效果提升,此外,对原始数据进行了FIG处理,实现了降维,算法运行时间得到减少,算法效率得到提升。
The original data is processed based on fuzzy information granulation, and the support vector machine (SVM) and differential evolution algorithm (DE) are used to establish the prediction model, and the residual correction is carried out according to the Markov chain. Predict the trend of gas concentration change. Experiments show that this method has improved compared with the direct of the granulation original data,in addition, FIG treatment, achieve dimensionality reduction algorithm running time is reduced, the algorithm efficiency is improved.
作者
武艳蒙
邱春荣
吕晓波
WU Yan-meng;QIU Chun-rong;LYU Xiao-bo(School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221008, China)
出处
《煤炭技术》
CAS
2018年第5期173-175,共3页
Coal Technology
基金
国家重点研发计划(2016YFC0801808)
关键词
煤矿
模糊信息粒化
支持向量机
差分进化算法
马尔科夫残差修正
coal mine
fuzzy information granulation
support vector machine
differential evolution algorithm
residual correction of Markov chain