• DocumentCode
    2829434
  • Title

    Residue Amending Combined Prediction Model Based on RBF Neural Network

  • Author

    Kong Li-Fang ; Zhang Hong ; Wang Zhe

  • Author_Institution
    Sch. of Inf. & Electr. Eng., China Univ. of Min. & Technol., Xuzhou, China
  • fYear
    2009
  • fDate
    11-13 Dec. 2009
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    The thesis introduces grey system model and RBF neural network. In the light of the drawbacks and merits of the two models, the author puts forward the residue amending combined prediction model, and makes a contrast between the three models in prediction and precision. The result indicates that, the combined model is better than that of the single models for higher precision and smaller error.
  • Keywords
    engines; grey systems; lubricating oils; mechanical engineering computing; radial basis function networks; wear; RBF neural network; engine lubricating oil analysis; grey system model; residue amending combined prediction model; wear metal analysis; Differential equations; Electronic mail; Engines; Linear regression; Lubricating oils; Machinery; Neural networks; Petroleum; Predictive models; Statistics;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-4507-3
  • Electronic_ISBN
    978-1-4244-4507-3
  • Type

    conf

  • DOI
    10.1109/CISE.2009.5364035
  • Filename
    5364035
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2829434