A Hopfield Network Learning Algorithm for Graph Planarization

摘要

A gradient ascent learning algorithm of the Hopfield neural networks for graph planarization is presented. This learning algorithm uses the Hopfield neural networks to get a near-maximal planar subgraph, and increases the energy by modifying parameters in a gradient ascent direction to help the network escape from the state of the near-maxmial planar sub- graph to the state of the maximal planar subgraph or better one.