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An Artificial Neural Network Application for Estimation of Natural Frequencies of Beams

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Academic year: 2017

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Fig. 1. Geometry of the beam
Fig. 4. Neuron weight adjustments  B.  Normalizing the data
TABLE III.  V ARIATIONS OF  N ATURAL  F REQUENCIES OF THE  P RISMATICAL  S TEEL  B EAMS  U NDER THE  F OUR  D IFFERENT  B OUNDARY  C ONDITIONS  V ERSUS
Fig. 7.  Scatter diagrams of Model 1 for tangent sigmoid transfer function with 8 neurons a) training b) test
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