Design of the inverse function delayed neural network for solving combinatorial optimization problems

Yoshihiro Hayakawa, Koji Nakajima

研究成果: Article査読

19 被引用数 (Scopus)

抄録

We have already proposed the inverse function delayed (ID) model as a novel neuron model. The ID model has a negative resistance similar to Bonhoeffervan der Pol (BVP) model and the network has an energy function similar to Hopfield model. The neural network having an energy can converge on a solution of the combinatorial optimization problem and the computation is in parallel and hence fast. However, the existence of local minima is a serious problem. The negative resistance of the ID model can make the network state free from such local minima by selective destabilization. Hence, we expect that it has a potential to overcome the local minimum problems. In computer simulations, we have already shown that the ID network can be free from local minima and that it converges on the optimal solutions. However, the theoretical analysis has not been presented yet. In this paper, we redefine three types of constraints for the particular problems, then we analytically estimate the appropriate network parameters giving the global minimum states only. Moreover, we demonstrate the validity of estimated network parameters by computer simulations.

本文言語English
論文番号5352267
ページ(範囲)224-237
ページ数14
ジャーナルIEEE Transactions on Neural Networks
21
2
DOI
出版ステータスPublished - 2010 2月

ASJC Scopus subject areas

  • ソフトウェア
  • コンピュータ サイエンスの応用
  • コンピュータ ネットワークおよび通信
  • 人工知能

フィンガープリント

「Design of the inverse function delayed neural network for solving combinatorial optimization problems」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル