WANG Shao-hua, LIU Jua. Selection Method of Variable of Standard Coal Consumption Rate Model of Thermal Power Generating Units[J]. Journal of Mine Automation, 2009, 35(3): 27-31.
Citation: WANG Shao-hua, LIU Jua. Selection Method of Variable of Standard Coal Consumption Rate Model of Thermal Power Generating Units[J]. Journal of Mine Automation, 2009, 35(3): 27-31.

Selection Method of Variable of Standard Coal Consumption Rate Model of Thermal Power Generating Units

More Information
  • The variable selection is foundation in modeling of neural network.There are many influencing factors in thermal power generating units,which affect the coal consumption rate.If all sorts of influencing factors are included in input variable,related input variables will become overabundance,increase training burden of neural network,enhance possibility to get in local minimum point and reduce prediction precision of neural network.The paper firstly analyzed the factors that affected the standard coal consumption rate of thermal power generating units and proposed a method of variable selection based on sensitivity analysis,then it used the method to calculate contributing rate of each factor for output,and according to each contributing rate,the method selected six factors which had the most contributing rate from many influencing factors as input variable of neural network model.The simulation result showed that the method of variable selection could simplify structure of neural network,reduce training time of neural network and improve prediction precision.
  • Related Articles

    [1]LEI Jing, YU Bin. Identification method of coal and rock based on information fusion and neural network[J]. Journal of Mine Automation, 2017, 43(9): 102-105. DOI: 10.13272/j.issn.1671-251x.2017.09.018
    [2]KOU Gui-yue, TANG He-sheng. Research of PID Control Based on Neural Network of Lift System of Excavator[J]. Journal of Mine Automation, 2010, 36(5): 47-50.
    [3]BIAN He-ying~, LI Hong-wei~. Predictive Control of Gas Recovery System Based on Neural Network[J]. Journal of Mine Automation, 2009, 35(8): 69-71.
    [4]FU Hua, LI Da-zhi. PID Self-tuning Control System Based on Neural Network[J]. Journal of Mine Automation, 2009, 35(7): 72-75.
    [5]ZHAO Wen-tao, YANG Jing. Research of Safety Information Management Model of Coal Mine Based on Data Mining[J]. Journal of Mine Automation, 2009, 35(7): 36-39.
    [6]CHEN Xi, FU Xing-wu, ZHANG Xing-yuan, CHEN Feng. Application of BP Neural Network in Error Compensation of Distance Measurement[J]. Journal of Mine Automation, 2008, 34(3): 57-58.
    [7]GUO Xiu-cai, SHU Huai-li. Temperature Control System Based on PID Neural Network[J]. Journal of Mine Automation, 2008, 34(3): 30-32.
    [8]SHU Huai-lin , GUO Xiu-cai . PID Neural Network Control System of Multi-variable and Strong-coupled Time-varying System[J]. Journal of Mine Automation, 2003, 29(5): 16-18.
    [9]LI Qiang, KONG Li, CHENG Wei, LIU Wen-zhong. Application of Modeling Approach Based on Fuzzy Neural Network in the Recognition System for Coal Gangues[J]. Journal of Mine Automation, 2002, 28(2): 10-13.
    [10]ZHENG Ming-fang , XIAO Li-chuan , XUE Guo-xin , L Si-wei . Optimization Control of Neuron Network for Water Level in Coal-burning Boiler[J]. Journal of Mine Automation, 2001, 27(6): 1-3.
  • Cited by

    Periodical cited type(2)

    1. 田宏彬. 选煤生产过程标准数据平台的建设. 煤炭加工与综合利用. 2022(11): 71-76 .
    2. 崔方方,石婷,黄磊,王永伟,兰亚佳,杨跃林. 模糊数学综合评价模型在制鞋业职业病危害风险评估中的应用. 现代预防医学. 2017(17): 3104-3108 .

    Other cited types(3)

Catalog

    Article Metrics

    Article views (29) PDF downloads (6) Cited by(5)
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return