Effectiveness of Scale Free Network to the Performance Improvement of a Morphological Associative Memory without a Kernel Image

Authors:
Takashi Saeki;Tsutomu Miki
Affiliations:
Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu, Japan 808-0196;Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu, Japan 808-0196
Venue:
Neural Information Processing
Year:
2007

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Abstract

In this paper, we present a new approach of the morphological associative memory (MAM) without a kernel image to reduce the network size by using the scale free network. The MAM is one of the powerful associative memories compared to ordinary associative memories. Weak point of the MAM is to need the kernel image which is susceptibility to noise and hard to design. We have already presented the MAM without a kernel image as a practical model. However the model has a drawback that the perfect recall rate is degraded. On the other hand, it has been reported that an introduction of the scale free networkto associative memories is effective in the improvement of the recall rate and the reduction of the network size. We try to reduce the network size and improve the recall rate by introducing the scale free network.