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一种用于两页交叉数问题的改进神经网络模型。

An improved neural network model for the two-page crossing number problem.

作者信息

He Hongmei, Sýkora Ondrej, Mäkinen Erkki

出版信息

IEEE Trans Neural Netw. 2006 Nov;17(6):1642-6. doi: 10.1109/TNN.2006.881486.

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.

摘要

最简单的图形绘制方法是将图的顶点放置在一条直线上,并将边绘制为直线上方或下方的半圆。这样的图称为两页书式图。图G的所有两页式图中交叉点的最小数量称为G的两页交叉数。Cimikowski和Shope通过使用具有2m个神经元的Hopfield网络解决了n个顶点和m条边的图的两页交叉数问题。我们在此提出一种具有m个神经元的改进的Hopfield模型。新模型在解的质量方面具有更好的性能,并且对于所有测试的图,其效率都比Cimikowski和Shope的模型更高。对于具有m个处理器的新Hopfield模型,该算法的并行时间复杂度(不考虑交叉数计算)为O(m),明显优于先前的算法。

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