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Chaotic Analog Associative Memory by Hisao Imai; Yuko Osana; Masafumi Hagiwara is a Computer Science article available to read on EtoBox.

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## Abstract This paper proposes chaotic analog associative memory (CAAM), which can handle one‐to‐many learning pairs composed of analog patterns. Most past associative memory models have considered a binary pattern as the pattern to be learned, which makes it difficult to handle analog patterns. It is also a problem that the superposed pattern is recalled due to interaction between the memorized patterns. Thus, it becomes difficult to handle the association of one‐to‐many learning pairs, in which multiple patterns can be recalled from a single given pattern. In contrast, the proposed model uses a multiwinner self‐organizing neural network (MWSONN), which can handle analog patterns, and realizes association of the analog patterns. In the proposed CAAM, the chaotic neuron is introduced as a part of the network, and one‐to‐many association is realized by utilizing the dynamic recall power of the chaotic neuron. Computer experiments verify that the association of one‐to‐many learning pairs composed of analog patterns can be realized, and that the proposed model has high noise immunity and robustness to faults. © 2005 Wiley Periodicals, Inc. Syst Comp Jpn, 36(4): 82–90, 2005; Published

Who reads Chaotic Analog Associative Memory?

Author

Hisao Imai; Yuko Osana; Masafumi Hagiwara

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); John Wiley & Sons Inc.; Wiley (ISSN 0882-1666)

Published

2005

Field

Computer Science (Physical Sciences)