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...
Guardado en:
Autores principales: | LIU Chenxu, YU Guilan |
---|---|
Formato: | article |
Lenguaje: | ZH |
Publicado: |
Editorial Office of Journal of Shanghai Jiao Tong University
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/e3147e33e4744f91a5d9666d1ddbd668 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Fluid-Structure Interaction Calculation Framework for Non-Rigid Airship Based on Explicit Dynamics
por: ZHANG Yu, et al.
Publicado: (2021) -
Full-scale self-propulsion simulation with a discretized propeller
por: Zhang Qingshan, et al.
Publicado: (2021) -
Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication
por: Yufei Liu, et al.
Publicado: (2021) -
Data Splitting Method of Distance Metric Learning Based on Gaussian Mixed Model
por: ZHENG Dezhong, et al.
Publicado: (2021) -
Network Intrusion Detection Based on Extended RBF Neural Network With Offline Reinforcement Learning
por: Manuel Lopez-Martin, et al.
Publicado: (2021)