Memory Superimposition by Backpropagation Neural Networks

機関リポジトリ HANDLE Web Site

書誌事項

タイトル別名
  • 誤差逆伝播ネットワークによる重ね書き記憶

この論文をさがす

抄録

application/pdf

We propose a novel neural network for incremental learning tasks where networks are required to learn new knowledge without forgetting the old one. An essential core of the proposed neural learning structure is a transferring scheme from short-term memory (STM) into long-term memory (LTM) as in brains by using dynamic changing weights. As the number of LTMs increases, a new network structure is superimposed on the previous one without disturbing the past LTMs by introducing a lateral inhibition mechanism. Superiority of the proposed neural structure to the conventional backpropagation networks is proven with respect to the learning ability.

紀要類(bulletin)

671882 bytes

収録刊行物

キーワード

詳細情報 詳細情報について

問題の指摘

ページトップへ