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  • The paper presents a study and comparison of static feedforward neural network performance for prediction of lung motion. A feedforward neural network with local and global optimization for prediction of lung respiration is presented. The applicability of the Levenberg-Marquardt algorithm and the back-propagation learning process are discussed. Sliding window learning for retraining static neural network is applied as a more efficient learning prediction method. Prediction results are presented and compared to demonstrate the effectiveness of the applied neural networks.
  • The paper presents a study and comparison of static feedforward neural network performance for prediction of lung motion. A feedforward neural network with local and global optimization for prediction of lung respiration is presented. The applicability of the Levenberg-Marquardt algorithm and the back-propagation learning process are discussed. Sliding window learning for retraining static neural network is applied as a more efficient learning prediction method. Prediction results are presented and compared to demonstrate the effectiveness of the applied neural networks. (en)
Title
  • Lung motion prediction by static neural networks
  • Lung motion prediction by static neural networks (en)
skos:prefLabel
  • Lung motion prediction by static neural networks
  • Lung motion prediction by static neural networks (en)
skos:notation
  • RIV/68407700:21220/10:00170327!RIV14-MSM-21220___
http://linked.open...avai/riv/aktivita
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  • S
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  • 269039
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  • RIV/68407700:21220/10:00170327
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  • Lung Tumor Motion; Prediction; Static Feed-forward Neural Network; Sliding Window; Levenberg-Marquardt Algorithm; Effectiveness (en)
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http://linked.open...ontrolniKodProRIV
  • [6C155F093A53]
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  • Praha
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  • Praha
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  • 4th IMECO International Symposium on Measurement, Analysis and Modelling of Human Functions - ISHF 10
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  • Bíla, Jiří
  • Bukovský, Ivo
  • Homma, N.
  • Rodriguez, Ricardo
  • Kei, I.
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number of pages
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  • České vysoké učení technické v Praze. Fakulta strojní
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  • 978-1-61738-984-9
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  • 21220
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