Improvement of the perfect recall rate of block splitting type morphological associative memory using a majority logic approach

Authors:
Takashi Saeki;Tsutomu Miki
Affiliations:
Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu, Japan;Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu, Japan
Venue:
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Year:
2006

Citing 3
Cited 1

Morphology neural networks: an introduction with applications

Circuits, Systems, and Signal Processing - Special issue: networks for neural processing
Morphological shared-weight networks with applications to automatic target recognition

IEEE Transactions on Neural Networks
Morphological associative memories

IEEE Transactions on Neural Networks

A morphological associative memory employing a reverse recall

ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a new improvement approach of the perfect recall rate of a block splitting type morphological associative memory (BMAM) is presented. The BMAM is one of MAMs without the kernel image, which is realized in more compact size as keeping the perfect recall rate as same as a normal MAM (without the kernel image). However, the MAM without kernel image has a problem that the perfect recall rate is inferior to a standard MAM (with the kernel image). Therefore, we try to improve the problem by a majority logic scheme and confirm the effectiveness of the proposed approach through autoassociation experiments of alphabet patterns compared to the traditional approaches in terms of the noise tolerance.