• 제목/요약/키워드: Arificial Neural Network

검색결과 3건 처리시간 0.017초

인공신경망기법을 이용한 굴착에 따른 지표침하평가 (Prediction of Deep Excavation-induced Ground surface movements using Artifical Neural Network)

  • 유충식;최병석
    • 한국지반공학회:학술대회논문집
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    • 한국지반공학회 2003년도 봄 학술발표회 논문집
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    • pp.69-76
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    • 2003
  • This paper presents the prediction of deep excavation-induced ground surface movements using artifical neural network(ANN) technique, which is of prime importance in the perspective of damage assessment of adjacent buildings. A finite element model, which can realistically replicate deep excavation-induced ground movements was employed to perform a parametric study on deep excavations with emphasis on ground movements. The result of the finite element analysis formed a basis for the Arificial Neural Network(ANN) system development. It was shown that the developed ANN system can be effecting used for a first-order prediction of ground movements associated with deep-excavation.

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WFSO 알고리즘을 이용한 인공 신경망과 합성곱 신경망의 학습 (Training Artificial Neural Networks and Convolutional Neural Networks using WFSO Algorithm)

  • 장현우;정성훈
    • 디지털콘텐츠학회 논문지
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    • 제18권5호
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    • pp.969-976
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    • 2017
  • 본 논문에서는 최적화 알고리즘으로 개발된 WFSO(Water Flowing and Shaking Optimization) 알고리즘을 사용한 인공신경망 과합성공 신경망의 학습 방법을 제안한다. 최적화 알고리즘은 다수의 후보 해를 기반으로 탐색해 나가기 때문에 일반적으로 속도가 느린 단점이 있으나 지역 최소값에 거의 빠지지 않고 병렬화가 용이하며 미분 불가능한 활성화함수를 갖는 인공신경망 학습도 가능하고 구조와 가중치를 동시에 최적화 할 수 있는 장점이 있다. 본 논문에서는 WFSO 알고리즘을 인공신경망 학습에 적용하는 방법을 설명하고 다층 인공신경망과 합성곱 신경망에서 오류역전파 알고리즘과 성능을 비교한다.

Development of an Adaptive Neuro-Fuzzy Techniques based PD-Model for the Insulation Condition Monitoring and Diagnosis

  • Kim, Y.J.;Lim, J.S.;Park, D.H.;Cho, K.B.
    • E2M - 전기 전자와 첨단 소재
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    • 제11권11호
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    • pp.1-8
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    • 1998
  • This paper presents an arificial neuro-fuzzy technique based prtial discharge (PD) pattern classifier to power system application. This may require a complicated analysis method employ -ing an experts system due to very complex progressing discharge form under exter-nal stress. After referring briefly to the developments of artificical neural network based PD measurements, the paper outlines how the introduction of new emerging technology has resulted in the design of a number of PD diagnostic systems for practical applicaton of residual lifetime prediction. The appropriate PD data base structure and selection of learning data size of PD pattern based on fractal dimentsional and 3-D PD-normalization, extraction of relevant characteristic fea-ture of PD recognition are discussed. Some practical aspects encountered with unknown stress in the neuro-fuzzy techniques based real time PD recognition are also addressed.

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