The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

The Korean Institute of Electrical Engineers (대한전기학회)
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Classification Performance Improvement of Steam Generator Tube Defects in Nuclear Power Plant Using Bagging Method

Bagging 방법을 이용한 원전SG 세관 결함패턴 분류성능 향상기법

  • 이준표 (숭실대 공대 전기공학부) ;
  • 조남훈 (숭실대 공대 전기공학부)
  • Published : 2009.12.01
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Abstract

For defect characterization in steam generator tubes in nuclear power plant, artificial neural network has been extensively used to classify defect types. In this paper, we study the effectiveness of Bagging for improving the performance of neural network for the classification of tube defects. Bagging is a method that combines outputs of many neural networks that were trained separately with different training data set. By varying the number of neurons in the hidden layer, we carry out computer simulations in order to compare the classification performance of bagging neural network and single neural network. From the experiments, we found that the performance of bagging neural network is superior to the average performance of single neural network in most cases.

Keywords

References

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