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

会議情報

主催: The Society of Instrument and Control Engineers

共催: 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

著者情報

キーワード: associative memory, Universal Learning Networks(ULNs), storage capacity, RasID

会議録・要旨集フリー

p. 162

詳細

抄録

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.

責任著者(Corresponding author)

J-STAGEへの登録はこちら（無料）