• DocumentCode
    3391108
  • Title

    Research on the neural networks and the geodetic number of the graph

  • Author

    Cao, Jianxiang ; Wu, Bin ; Shi, Minyong

  • Author_Institution
    Sch. of Comput. Sci., Commun. Univ. of China, Beijing, China
  • fYear
    2009
  • fDate
    15-17 June 2009
  • Firstpage
    418
  • Lastpage
    421
  • Abstract
    Graph theory is the fundamental basis of the neural networks. This paper reports the investigation work of the relationships between artificial neural networks and graph theory, and presents the analysis of the specific issues relating to the change of the geodetic number due to operations on the graphs. Recent research work on the geodetic number of graphs has been found in the literature. Determining the geodetic number of arbitrary graph can be proved to be NP-hard. However, a fundamental issue in graph theory concerns how a parameter value is affected after small changes executed to the graph. This issue is analyzed in the authors´ research by adding or contracting an edge to the graph.
  • Keywords
    computational complexity; graph theory; neural nets; NP-hard problem; artificial neural networks; geodetic number; graph theory; Animation; Artificial neural networks; Biological neural networks; Biological system modeling; Computer science; Feedforward neural networks; Graph theory; Neural networks; Neurons; Software libraries; geodesic; geodetic number; geodetic set; neural network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cognitive Informatics, 2009. ICCI '09. 8th IEEE International Conference on
  • Conference_Location
    Kowloon, Hong Kong
  • Print_ISBN
    978-1-4244-4642-1
  • Type

    conf

  • DOI
    10.1109/COGINF.2009.5250702
  • Filename
    5250702