摘要
对煤流量短时间内的趋势进行预测,是实现带速与运量匹配的前提条件,而现有输送带煤流量短时预测方法存在实时性不好和精度不高的问题。针对上述问题,提出了一种基于支持向量机的输送带煤流量短时预测方法。该方法首先利用支持向量机算法选择实时煤流量作为因变量、统计数据时间作为自变量,然后对实际采集到的煤流量数据进行归一化处理,利用交叉验证方法选择出最优的参数,利用最优参数训练支持向量机,拟合出理想的短时间内煤流量预测曲线,最后通过进一步对比拟合均方误差、相关系数等预测指标来分析煤流量预测曲线与原始数据曲线的拟合程度,得到最佳预测曲线。Matlab仿真结果表明,该方法能够较好地预测输送带短时间内的煤流量,预测数据与真实值之间的偏差很小,均方误差为0.000 152 563,相关系数为99.784 8%。
Forecasting the trend of coal flow in a short-term is the precondition to realize matching of belt speed and traffic volume.However,the existing short-term prediction methods of coal flow on conveyor belt have the problems of insufficient real-time performance and low precision.For the problems,a short-term prediction method of coal flow on conveyor belt based on support vector machine was proposed.Firstly,the method uses support vector machine algorithm to select the real-time coal flow as dependent variable and statistical data time as independent variable,and then normalizes actual collected coal flow data,uses cross-validation method to select the best parameters.It also uses the best parameters to train the support vector machine to fit ideal short-term prediction curves of coal flow.Finally,the method analyzes fitting degree of the coal flow prediction curves and the original data curves by further comparing the prediction parameters such as mean square error and correlation coefficient to obtain the best prediction curve.The Matlab simulation results show that the method can predict coal flow on conveyor belt in a short time,and the deviation between the predicted data and the true value is small,the mean square error is 0.000 152 563,and the correlation coefficient is 99.784 8%.
作者
冯梅
乔铁柱
FENG Mei;QIAO Tiezhu(Key Laboratory of Advanced Transducers and Intelligent Control System,Ministry of Education,Taiyuan University of Technology,Taiyuan 030024,China)
出处
《工矿自动化》
北大核心
2019年第4期72-76,共5页
Journal Of Mine Automation
基金
山西省自然科学基金项目(201601D011059)
关键词
矿用带式输送机
煤流量短时预测
带速与运量匹配
回归预测
支持向量机
因变量
自变量
mine-used belt conveyor
short-term prediction of coal flow
matching of belt speed and traffic volume
regression prediction
support vector machine
dependent variable
independent variable
