Prediction of Energy Transmission Spectrum of Layered Periodic Structures by Neural Networks
In this paper, the prediction of the energy transmission spectrum for layered periodic structures is studied. By considering three cases of geometric parameters and physical parameters changing individually or simultaneously, a deep back propagation (BP) neural network is constructed to realize accu...
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| Auteurs principaux: | , |
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| Format: | article |
| Langue: | ZH |
| Publié: |
Editorial Office of Journal of Shanghai Jiao Tong University
2021
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| Sujets: | |
| Accès en ligne: | https://doaj.org/article/e3147e33e4744f91a5d9666d1ddbd668 |
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| Résumé: | In this paper, the prediction of the energy transmission spectrum for layered periodic structures is studied. By considering three cases of geometric parameters and physical parameters changing individually or simultaneously, a deep back propagation (BP) neural network is constructed to realize accurate prediction of the energy transmission spectrum of layered periodic structure. A comparison of the predicted results with those obtained by the radial basis function (RBF) neural network verifies the effectiveness of the proposed method. |
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