• DocumentCode
    816196
  • Title

    An Improved Neural Network Model for the Two-Page Crossing Number Problem

  • Author

    Hongmei He ; Sykora, O. ; Makinen, E.

  • Author_Institution
    Dept. of Comput. Sci., Loughborough Univ.
  • Volume
    17
  • Issue
    6
  • fYear
    2006
  • Firstpage
    1642
  • Lastpage
    1646
  • Abstract
    The simplest graph drawing method is that of putting the vertices of a graph on a line and drawing the edges as half-circles either above or below the line. Such drawings are called two-page book drawings. The smallest number of crossings over all two-page drawings of a graph G is called the two-page crossing number of G. Cimikowski and Shope have solved the two-page crossing number problem for an n-vertex and m-edge graph by using a Hopfield network with 2 m neurons. We present here an improved Hopfield model with m neurons. The new model achieves much better performance in the quality of solutions and is more efficient than the model of Cimikowski and Shope for all graphs tested. The parallel time complexity of the algorithm, without considering the crossing number calculations, is O(m) for the new Hopfield model with m processors clearly outperforming the previous algorithm
  • Keywords
    Hopfield neural nets; computational complexity; graph theory; Hopfield network; improved neural networks; learning algorithm; parallel time complexity; simplest graph drawing method; two-page crossing number problem; Books; Costs; Equations; Large scale integration; Neural networks; Neurons; Semiconductor device modeling; Testing; Very large scale integration; Energy function; Hopfield model; learning algorithm; motion equation; two-page crossing number; Algorithms; Information Storage and Retrieval; Neural Networks (Computer); Pattern Recognition, Automated;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/TNN.2006.881486
  • Filename
    4012025