期刊文献+

数据驱动方法在流程工业中的应用命题综述 被引量:3

An Overview of Data Driven Methods in Process Industries
下载PDF
导出
摘要 相对于具体技术方案,数据驱动方法在工业应用取得成功首先取决于应用命题本身。从流程工业应用的角度,对涉及数据驱动方法的应用命题进行了提炼和分类,并用典型的例子和文献将这些类别的应用命题具体化,包括应用命题背景、目标的简单描述和数据驱动方法在解决这些命题时所担负的角色。同时,还对数据驱动方法的流程工业应用研究做了展望,提出了数据完备性问题。 The success involving with the applications of data driven methods in industries depends on the application issue itself rather than technical solutions.The applications of data driven methods in process industries were reviewed with the perspective of application subjects.These subjects were classified and described by application examples,including subjects' background,targets and the roles data driven methods played in.Additionally,a discussion was made on future research,especially the problem of constructing complete data source was proposed.
作者 顾海杰 荣冈
出处 《化工自动化及仪表》 CAS 北大核心 2009年第5期1-6,共6页 Control and Instruments in Chemical Industry
基金 国家"863"计划资助项目(2007AA040702 2007AA04Z191)
关键词 数据挖掘 数据驱动方法 流程工业 工业应用 data mining data driven method process industry industrial application
  • 相关文献

参考文献5

二级参考文献30

  • 1裴瑞凌,荣冈.炼油过程的智能工厂流程模拟仿真平台[J].化工自动化及仪表,2005,32(2):43-46. 被引量:14
  • 2黄建华,金滨,贾绍明.材料价格上涨对公路造价影响的预测和对策[J].中南公路工程,2005,30(3):141-142. 被引量:23
  • 3刘浪.影响高等级公路造价的因素研究[J].重庆交通学院学报,2006,25(1):90-92. 被引量:20
  • 4[1]Hancke G P,Malan R.On-line particle size distribution analysis of pulverised coal[C]//Industrial Electronics,ISIE'96,Proceedings of the IEEE International Symposium on 1996,2:1066-1070. 被引量:1
  • 5[2]Hancke G P,Malan R.A modal analysis technique for the on-line particle size measurement of pneumatically conveyed pulverized coal[J].IEEE Transactions On Instrumentation And Measurement,1998,47(1):114-122. 被引量:1
  • 6[3]Casali A,Gonzalez G,Vallebuona G,et al.Grindablity soft-sensors based on lithological composition and on-line measurements[J].Minerals Engineering,2001,14(7):689-700. 被引量:1
  • 7[4]Gonzalez G D,Orchard M,Cerda J L,et al.Local models for soft-sensors in a rougher flotation bank[J].Minerals Engineering,2003,16(5):441-453. 被引量:1
  • 8[6]Suykens J A K,Vandewalle J.Least squares support vector machine classifiers[J].Neural Processing Letters,1999,9(3):293-300. 被引量:1
  • 9[7]Rameswar Debnath,Masakazu Muramatsu,Haruhisa Takahashi.An efficient support vector machine learning method with second-order cone programming for large-scale problem[J].Applied Intelligence,2005,23(3):219-239. 被引量:1
  • 10[8]Gestel T V,Suykens J A K,Baesens B,et al.Benchmarking least squares support vector machine classifiers[J].Machine Learning,2004,54(1):5-32. 被引量:1

共引文献23

同被引文献23

  • 1邵克勇,范欣,张永华,田野.一种基于数据驱动的模糊系统建模方法[J].化工自动化及仪表,2009,36(5):25-28. 被引量:7
  • 2Huang B, Kadali R. Dynamic Modeling, Predic-tive Control and Performance Monitoring: a Data-driven Subspace Approach [ M ]. London : Springer, 2008. 被引量:1
  • 3Huang B, Ding S X, Qin S J. Closed-loop Subspace Identification:an Orthogonal Projection Approach [ J ]. Journal of Process Control ,2005,15 ( 1 ) :53 - 66. 被引量:1
  • 4Wang J,Qin S J. A New Subspace Identification Approach Based on Principal Component Analysis [ J ]. Journal of Process Control,2002,12( 8 ) :841 - 855. 被引量:1
  • 5Weixin Yao,Qin Wang.??Robust variable selection through MAVE(J)Computational Statistics and Data Analysis . 2013 被引量:1
  • 6杨志安,王光瑞,陈式刚.??用等间距分格子法计算互信息函数确定延迟时间(J)计算物理. 1995(04) 被引量:1
  • 7Shannon C E,Weaver W.The Mathematical Theory of Communication. . 1971 被引量:1
  • 8Darudi A,Rezaeifar S,Bayaz M H J D.Partial Mutual Information Based Algorithm for Input Variable Selection. 13th International Conference on Environment and Electrical Engineering . 2013 被引量:1
  • 9Dridi N,Giremus A,Giovannell J F,et al.Variable Selection for Noisy Data Applied in Proteomics. IEEE International Conference on Acoustics,Speech and Signal Processing . 2014 被引量:1
  • 10Jerome H. Friedman.Multivariate adaptive regression splines. The Annals of Statistics . 1991 被引量:1

引证文献3

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部