Morphological Associative Memories Based on Fuzzy Sets

DOI

Bibliographic Information

Other Title
  • ファジィ集合を用いた形態学的連想記憶について

Abstract

Research on associative memory is attracting attention. However, we were beginning to feel the limits of the performance of the conventional associative memory that is typified by Hopfield's model. A morphological associative memory is expected to be superior in performance, because there is no limit to the memory capacity as the associative memory. Ritter proposed morphological associative memories represented by W and M. The memory W is robust under erosive noise and M is robust under dilative noise. However, these memories are not robust under general noise that includes both erosive and dilative noises. We present a morphological associative memory that is applied the idea of the fuzzy sets to the calculation process. In this method, we need to find nucleuses of the learning patterns called kernel. Through numerical experiments, we examine the relationships between the number of kernel bits and recalling rate of the learning patterns.

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Details 詳細情報について

  • CRID
    1390282680648238976
  • NII Article ID
    130005035284
  • DOI
    10.14864/fss.24.0.51.0
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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