Learning of a Multi-Valued Neural Network and Its Application : Special Section on JTC-CSCC'92

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概要

• 論文の詳細を見る

• A learning procedure of a three layer neural network with limited structure, called a multi-valued neural network, is proposed. The three layer net has a single linear neuron in its output layer. All input weights of a number of hidden neurons are identical. The network takes k+1 distinct stable values, where k is the number of hidden neurons. The proposed learning procedure consists of two parts, Phase I and Phase II. The former is one for the learning of weights between the hidden and output layers, and the latter is one for those between the input and the hidden layers. The network is applied to classification of numerals, which shows the effectiveness of the proposed learning procedure.

• 社団法人電子情報通信学会の論文
• 1993-06-25

著者

• Takiyama Ryuzo

  The Department Of Visual Communication Design Kyushu University Of Design Science

• Kubo Koichiro

  NTT Network Information Systems Laboratories

関連論文

• A Differential-Geometrical Theory of Sensory System Relations between the Psychophysical, the DL and the JND Functions (Special Section on Neural Nets, Chaos and Numerics)
• Learning of a Multi-Valued Neural Network and Its Application : Special Section on JTC-CSCC'92
• Error-Correction Learning of Three Layer Neural Networks Based on Linear-Homogeneous Expressions

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