Premium
Chaotic complex‐valued bipartite auto‐associative memory with a periodic activation function
Author(s) -
Kobayashi Masaki
Publication year - 2017
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.22414
Subject(s) - chaotic , content addressable memory , bipartite graph , associative property , noise (video) , computer science , function (biology) , binary number , algorithm , artificial intelligence , theoretical computer science , mathematics , artificial neural network , arithmetic , pure mathematics , graph , evolutionary biology , biology , image (mathematics)
A Hopfield associative memory (HAM) can store binary data. A HAM has been extended to a complex‐valued HAM (CHAM) which can store multi‐level data. A CHAM has often been applied to the storage of grayscale images. A CHAM has a problem of rotational invariance, which makes many psuedomemories and reduces the noise tolerance. A chaotic CHAM can explore the stored patterns, though it recalls many pseudomemories. A complex‐valued bipartite auto‐associative memory (CBAAM) was proposed to improve the noise tolerance by solving rotational invariance. Since a CBAAM stores the reversed patterns, a chaotic CBAAM (CCBAAM) recalls both the training and reversed patterns. In order to solve this problem, a periodic activation function is introduced. A periodic activation function identifies the training and reversed patterns, so that a CCBAAM does not recall the reversed patterns. We evaluate our proposed CCBAAM by computer simulations. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.