A Reasoning and Learning Method for Fuzzy Rules with Associative Memory

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  • 連想記憶によるファジールールの推論・学習方式
  • レンソウ キオク ニ ヨル ファジールール ノ スイロン ガクシュウ ホウシキ

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Abstract

In this paper, a reasoning and learning method of fuzzy rules, which employs associative memories, is presented. Fuzzy rules are described by neural networks, such that a proposition and an IF-THEN relation in rules are stored in a layered neural network, and a bidirectional connected neural network implemented in an associative memory, respectively. Fuzzy reasoning is performed by the dynamical changes in the associative memory. A learning functions is provided to add and correct fuzzy rules. An application of this method to a performance estimation for elevator group control systems is reported.

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