分子の構造活性相関解析のためのニューラルネットワークシミュレータ:Necoの開発 (3)  組み合わせモデルとパーセプトロンの性能比較

書誌事項

タイトル別名
  • Development of NEural network simulator for structure-activity COrrelation of molecules:Neco. (3). Performance Evaluation of Self-organized Network and Perceptron.
  • ブンシ ノ コウゾウ カッセイ ソウカン カイセキ ノ タメ ノ ニューラル
  • Performance Evaluation of Self-organized Network and Perceptron
  • 組み合わせモデルとパーセプトロンの性能比較

この論文をさがす

抄録

A Self-organized network model for high-speed learning was included in the perceptron type Neural network simulator for structure-activity correlation of molecules : Neco. The performance of the Self-organized network model was compared with that of perceptron using twodimensional exclusive OR problem and the relationship between 13C-NMR shift and the conformation of norbornane. For practical use, the speed for convergence of the Self-organized network is almost four times faster than that of perceptron though perceptron gives higher order convergence. In the case of 13C-NMR shift and conformation of norbornane, a Self-organized network seems to show strong nonlinear classification in comparsion with perceptron.

収録刊行物

被引用文献 (8)*注記

もっと見る

参考文献 (10)*注記

もっと見る

詳細情報 詳細情報について

問題の指摘

ページトップへ