CNN with Multi-Level Hysteresis Quantization Output
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概要
- 論文の詳細を見る
This paper presents a novel class of cellular neural networks, where the output is given by the multi-level hysteresis quantization function. Since each cell of elementary CNN has bi-stable piecewise linear function, the image processing is restricted in black and white case. Hence, the architecture provided in this paper would extend availability of CNN. Especially, it is extremely useful for image intensity conversion. In this paper, the Lyapunov stability of CNN with multi-level hysteresis quantization output is proven and the computer simulation shows good convergence property of the CNN.
- 社団法人電子情報通信学会の論文
- 2000-03-18
著者
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TANJI Yuichi
Dept. of Electrical and Electronic Engineering, Tokushima University
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Tanji Yuichi
Dept.of Electrical And Electronic Engineering Sophia University
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Tanji Yuichi
Dept. Of Electrical And Electronic Engineering Sophia University
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TANAKA Mamoru
Dept. of Electrical and Electronic Engineering, Sophia University
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YOKOSAWA Kenichi
Dept.of Electrical and Electronic Engineering, Sophia University
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Yokosawa Kenichi
Dept.of Electrical And Electronic Engineering Sophia University
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Tanaka Mamoru
Dept. Of Electrical And Electronic Engineering Sophia University
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Tanaka Mamoru
Dept.of Electrical And Electronic Engineering Sophia University
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Tanaka Mamoru
Dept. of Electrical & Electronics Engineering, Sophia University
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