• DocumentCode
    502780
  • Title

    Undergraduate practice teaching job quality assessment based on artificial fish-BP neural network

  • Author

    Gao, Yongge ; Zhang, Yanhong ; Li, Lihua

  • Author_Institution
    Hebei Univ. of Eng., Handan, China
  • Volume
    2
  • fYear
    2009
  • fDate
    8-9 Aug. 2009
  • Firstpage
    379
  • Lastpage
    382
  • Abstract
    In order to solve the problems in undergraduate practice teaching job, the evaluation simulation model was set up using artificial fish-swarm-neural network; taking the experimental teaching, practice teaching, graduate design thesis, laboratory management and equipment for the input layer, the undergraduate practice teaching job quality for the output layer, establishing artificial neural network model and training and testing for the network using actual data. Practice shows that the model has better recognition accuracy. Finally, the assessment results digitized came to the conclusion that can be accurately, intuitively reflect the merits of the undergraduate theory teaching job quality, thus demonstrating that the artificial fish-BP neural network has broad prospects the evaluation at the undergraduate practice teaching job quality of colleges and universities.
  • Keywords
    artificial intelligence; backpropagation; engineering education; mobile robots; BP neural network; artificial fish; artificial neural network model; evaluation simulation model; swarm-neural network; undergraduate practice teaching job quality assessment; Artificial neural networks; Education; Educational institutions; Engineering management; Laboratories; Management training; Marine animals; Neurons; Quality assessment; Quality management; artificial fish-BP neural network; job quality assessment component; undergraduate prctice teaching;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computing, Communication, Control, and Management, 2009. CCCM 2009. ISECS International Colloquium on
  • Conference_Location
    Sanya
  • Print_ISBN
    978-1-4244-4247-8
  • Type

    conf

  • DOI
    10.1109/CCCM.2009.5267921
  • Filename
    5267921