조명전기설비학회논문지 (Journal of the Korean Institute of Illuminating and Electrical Installation Engineers)

  • 제26권9호
  • /
  • Pages.73-79
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  • 2012
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  • 1229-4691(pISSN)
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  • 2287-5034(eISSN)
한국조명전기설비학회 (The Korean Institute of IIIuminating and Electrical Installation Engineers)
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다중겹 교차검증 기법을 이용한 증기세관 결함크기 예측을 위한 신경회로망 성능 향상

Improvement of Neural Network Performance for Estimating Defect Size of Steam Generator Tube using Multifold Cross-Validation

  • 김남진 (숭실대학교 전기공학부) ;
  • 지수정 (숭실대학교 전기공학부) ;
  • 조남훈 (숭실대학교 전기공학부)
  • Kim, Nam-Jin (Soongsil University, Dept. Electrical Engineering) ;
  • Jee, Su-Jung (Soongsil University, Dept. Electrical Engineering) ;
  • Jo, Nam-Hoon (Soongsil University, Dept. Electrical Engineering)
  • 투고 : 2012.08.20
  • 심사 : 2012.09.13
  • 발행 : 2012.09.30
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초록

In this paper, we study on how to determine the number of hidden layer neurons in neural network for predicting defect size of steam generator tube. It was reported in the literature that the number of hidden layer neurons can be efficiently determined with the help of cross-validation. Although the cross-validation provides decent estimation performance in most cases, the performance depends on the selection of validation set and rather poor performance may be led to in some cases. In order to avoid such a problem, we propose to use multifold cross-validation. Through the simulation study, it is shown that the estimation performance of defect width (defect depth, respectively) attains 94% (99.4%, respectively) of the best performance achievable among the considered neuron numbers.

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참고문헌

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