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Using Higher Order Neuron on the Supervised Learning Machine of Kohonen Feature Map  

Jung, Jong-Soo (경인여자대학교)
Hagiwara, Masafumi (일본 KEIO대학 전기공학과)
Publication Information
The Transactions of the Korean Institute of Electrical Engineers D / v.52, no.5, 2003 , pp. 277-282 More about this Journal
Abstract
In this paper we propose Using Higher Order Neuron on the Supervised Learning Machine of the Kohonen Feature Map. The architecture of proposed model adopts the higher order neuron in the input layer of Kohonen Feature Map as a Supervised Learning Machine. It is able to estimate boundary on input pattern space because or the higher order neuron. However, it suffers from a problem that the number of neuron weight increases because of the higher order neuron in the input layer. In this time, we solved this problem by placing the second order neuron among the higher order neuron. The feature of the higher order neuron can be mapped similar inputs on the Kohonen Feature Map. It also is the network with topological mapping. We have simulated the proposed model in respect of the recognition rate by XOR problem, discrimination of 20 alphabet patterns, Mirror Symmetry problem, and numerical letters Pattern Problem.
Keywords
Kohonen Feature Map; Higher Order Neuron; XOR; Pattern; Mirror Symmetry; Numerical Letters;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
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