Auto Correlation Associative Memory by Universal Learning Networks (ULNs)

Keiko Shibuta; Kotaro Hirasawa; Jinglu Hu; Junichi Murata

doi:10.11499/sicep.2002.0.162.0

SICE Annual Conference 2002

DOI https://doi.org/10.11499/sicep.2002.0.162.0

Conference information

Host: The Society of Instrument and Control Engineers

Co-host: IEEE/Industiral Electronic Society, IEEE/Robotics and Automation Society, IEEE/Control System Society

Auto Correlation Associative Memory by Universal Learning Networks (ULNs)

Keiko Shibuta, Kotaro Hirasawa, Jinglu Hu, Junichi Murata

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Keywords: associative memory, Universal Learning Networks(ULNs), storage capacity, RasID

CONFERENCE PROCEEDINGS FREE ACCESS

Pages 162

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Abstract

In this paper, we propose a new auto correlation associative memory using Universal Learning Networks (ULNs). It enables not only to obtain associative memory by optimizing parameters but also to store more memories than conventional models by introducing “don’t care nodes” or “sensitivity term”. This is expected to settle some problems related to associative memory.

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