Learning Sequential Patterns by Nonmonotone Analog Neural Networks

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概要

• 論文の詳細を見る

• A learning method for nonmonotone analog neural networks is presented by which almost any sequential pattern can be memorized. This method does not require a complex learning rule or particular devices for synchronizing neurons or delay; one only has to change the input pattern gradually and modify the synaptic weights according to a kind of correlation learning rule. Then the state of the network follows a little after the input pattern, its trajectory growing into a dynamic attractor with a few times of repetition. Numerical simulations are performed to examine the learning process and the recollection ability of the model.

• 日本神経回路学会の論文

著者

• 森田 昌彦

  筑波大学電子・情報処理系

• 森田 昌彦

  筑波大学電子・情報工学系

関連論文

• 非単調神経回路網による時系列パターンの認識
• Learning Sequential Patterns by Nonmonotone Analog Neural Networks

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