Adaptation Strength According to Neighborhood Ranking of Self-Organizing Neural Networks(<Special Section>Nonlinear Theory and Its Applications)

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概要

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• In this paper we treat a novel adaptation strength according to neighborhood ranking of self-organizing neural networks with the objective of avoiding the initial dependency of reference vectors, which is related to the strength in the neural-gas network suggested by Martinetz et al. The present approach exhibits the effectiveness in the average distortion compared to the conventional technique through numerical experiments. Furthermore the present approach is applied to image data and the validity in employing as an image coding system is examined.

• 社団法人電子情報通信学会の論文
• 2002-09-01

著者

• MAEDA Michiharu

  the Department of Control & Information Systems Engineering at Kurume National College of Technology

• Miyajima Hiromi

  The Faculty Of Engineering Kagoshima University

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• Some Characteristics of Higher Order Neural Networks with Decreasing Energy Functions (Special Section on Nonlinear Theory and its Applications)
• An Adaptive Learning and Self-Deleting Neural Network for Vector Quantization
• Adaptation Strength According to Neighborhood Ranking of Self-Organizing Neural Networks(Nonlinear Theory and Its Applications)

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