• Title/Summary/Keyword: Arificial Neural Network

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

  • 유충식;최병석
    • Proceedings of the Korean Geotechical Society Conference
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    • 2003.03a
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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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Training Artificial Neural Networks and Convolutional Neural Networks using WFSO Algorithm (WFSO 알고리즘을 이용한 인공 신경망과 합성곱 신경망의 학습)

  • Jang, Hyun-Woo;Jung, Sung Hoon
    • Journal of Digital Contents Society
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    • v.18 no.5
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    • pp.969-976
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    • 2017
  • This paper proposes the learning method of an artificial neural network and a convolutional neural network using the WFSO algorithm developed as an optimization algorithm. Since the optimization algorithm searches based on a number of candidate solutions, it has a drawback in that it is generally slow, but it rarely falls into the local optimal solution and it is easy to parallelize. In addition, the artificial neural networks with non-differentiable activation functions can be trained and the structure and weights can be optimized at the same time. In this paper, we describe how to apply WFSO algorithm to artificial neural network learning and compare its performances with error back-propagation algorithm in multilayer artificial neural networks and convolutional neural networks.