Associative Neural Network Models Based on a Measure of Manhattan Length (Special Section on the 5th Karuizawa Workshop on Circuits and Systems)
スポンサーリンク
概要
- 論文の詳細を見る
In this paper, two models for associative memory based on a measure of manhattan length are proposed. First, we propose the two-layered model which has an advantage to its implementation by using PDN. We also refer to the way to improve the recalling ability of this model against noisy input patterns. Secondly, we propose the other model which always recalls the nearest memory pattern in a measure of manhattan length by lateral inhibition. Even if a noise of input pattern is so large that the first model can not recall, this model can recall correctly against such a noisy pattern. We also confirm the performance of the two models by computer simulations.
- 社団法人電子情報通信学会の論文
- 1993-03-25
著者
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OHTA Masaya
College of Engineering, University of Osaka Prefecture
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Ueda H
Teikyo Univ. Kanagawa Jpn
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Ogihara A
Osaka Prefecture Univ. Sakai‐shi Jpn
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Ogihara Akio
the College of Engineering, University of Osaka Prefecture
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Ohta Masaya
College Of Engineering University Of Osaka Prefecture
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Ueda Hiroshi
the College of Engineering, University of Osaka Prefecture
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Ohta Masaya
the College of Engineering, University of Osaka Prefecture
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Anzai Yoichiro
the Vehicular Techonlogy Lab., Sumitomo Electric Industries, Ltd.,
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Yoneda Shojiro
the Faculty of Environment Information Science, Mukogawa Women's University
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Ueda H
Usa Marine Biological Institute Kochi University
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Yoneda Shojiro
The Faculty Of Environment Information Science Mukogawa Women's University
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Anzai Yoichiro
The Vehicular Techonlogy Lab. Sumitomo Electric Industries Ltd.
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Ogihara Akio
the College of Engineering, Osaka Prefecture University
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