| 作成者 |
|
| 本文言語 |
|
| 出版者 |
|
|
|
| 発行日 |
|
| 収録物名 |
|
| 巻 |
|
| 出版タイプ |
|
| アクセス権 |
|
| 関連DOI |
|
|
|
| 関連URI |
|
|
|
| 関連情報 |
|
|
|
| 概要 |
A learning algorithm for single layer perceptrons is proposed. First, cone-like domains, each of which i s mapped by the perceptron network into almost an associative pattern, are derived. The learnin...g algorithm is obtained as a process that enlarges the cone-like domains. For autoassociative networks, it is shown that the cone-like domains become domains of attraction for stored patterns in the network. In this case, extended domains of attraction are also obtained by feeding t h e outputs of the network back to the input layer. I n computer simulations, character recognition ability of the autoassociative network i s examined.続きを見る
|
Mendeley出力
doi-tr-126