计算机工程与应用Issue(16)：1-5,54,6.DOI:10.3778/j.issn.1002-8331.1503-0247
回采工作面瓦斯涌出量耦合预测模型研究
摘要
Abstract
In order to predict the gas emission of working face accurately and quickly, a prediction model for gas emission of working face is put forward based on Principal Component Analysis(PCA), Modified Fruit Fly Optimization Algorithm (MFOA)and optimized Support Vector Machine(SVM). PCA is used for dimensionality reduction of original data, elimi-nating data redundancy, MFOA is used to optimize SVM parameters, avoids the negative impact of the model prediction results affected by selection of SVM parameters. Eventually, coupling prediction model is established based on PCA-MFOA-SVM, and simulation forecast is done as example by taking actual monitoring data. Results show that the mean absolute error of this model prediction is 0.077 5 m3/t, the mean relative error is 1.323 7%. Comparing with other models this model can realize the dynamic prediction of gas emission in the working face with its higher prediction accuracy and better comprehensive performance.关键词
瓦斯涌出量/主成分分析法/改进的果蝇优化算法/仿真预测Key words
gas emission quantity/Principal Component Analysis(PCA)/Modified Fruit flies Optimization Algorithm (MFOA)/simulation and forecast分类
信息技术与安全科学引用本文复制引用
李胜,韩永亮,李军文..回采工作面瓦斯涌出量耦合预测模型研究[J].计算机工程与应用,2015,(16):1-5,54,6.基金项目
国家自然科学基金项目（No.51004063）；辽宁省高等学校优秀人才支持计划（辽宁省教育厅，No.LJQ2011029）。 （）
