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
Link To Document :
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