@techreport{oai:ipsj.ixsq.nii.ac.jp:00051204,
 author = {畝見, 達夫 and Tatsuo, Unemi},
 issue = {32(1990-ICS-070)},
 month = {May},
 note = {比較的粒度の小さな離散時系列を入出力データとする一種の学習メカニズムを提案し，簡単な試験例題への応用を通して解説する．各ステップの入力データおよび出力データは，その時間順序に従った連想リンクによって交互に結合され記憶される．また，過去の記憶と現在の入力を比較し，類似度の高い記憶を想起表と呼ばれる記憶領域に蓄える．この想起表中のデータを元に予想を行ない，価値の高い入力データに行き着く経験の列をプランとして採用する．想起および予想の過程では，リハーサルに基づく記憶の強化が行なわれ，重要な記憶が生き残る．, This paper proposes a learning mechanism for which input/ouput data are formed in discrete time-sequence. Each of input and output data is memorized. in a node connected in the order of time. The learner stores parts of its experience which have a relatively high correlation with current input into a working memory named recalling table. Referring this table, it makes prediction to employ a sequence of action as a plan. During the process of recalling and prediction, a rehearsal-based strengthening of memory is done to let important data survive in the memory.},
 title = {予測と反省に基づく時系列の暗記学習},
 year = {1990}
}