• DocumentCode
    480629
  • Title

    Application Genetic Neural Network in Lithology Recognition and Prediction: Evidence from China

  • Author

    Shao, Yuxiang ; Chen, Qing

  • Author_Institution
    Sch. of Comput. Sci. & Technol., China Univ. of Geosci., Wuhan
  • Volume
    2
  • fYear
    2008
  • fDate
    20-22 Dec. 2008
  • Firstpage
    536
  • Lastpage
    539
  • Abstract
    The BP neural network algorithm has characteristics of slow convergence speed and local minimum value which could cause the loss of global optimal solution. In order to eliminate the shortcoming of BP neutral network algorithm, genetic algorithm is been put forward to optimize authority value and threshold value of BP nerve network. This paper establishes genetic neural network model. Study has been conducted on lithology recognition prediction using genetic neutral network model. The result shows that this model has significant advantages inspect of fast convergence speed, good generalization ability and not easy to yield minimal local results. In generally, this model exhibits good representation and strong prediction ability, and is suitable for recognition of lithology, lithofacies and sedimentary facies as well as geological research like deposit prediction and rock and mineral recognition.
  • Keywords
    backpropagation; genetic algorithms; geophysics computing; neural nets; rocks; sediments; China; backpropagation; genetic algorithm; genetic neural network; lithofacies; lithology prediction; lithology recognition; mineral recognition; rock recognition; sedimentary facies; Application software; Character recognition; Computer science; Convergence; Genetic algorithms; Geology; Mathematical model; Minerals; Neural networks; Predictive models; BP neural network; genetic algorithm; lithology recognition; optimization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Information Technology Application, 2008. IITA '08. Second International Symposium on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-0-7695-3497-8
  • Type

    conf

  • DOI
    10.1109/IITA.2008.432
  • Filename
    4739822