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...
Enregistré dans:
Auteurs principaux: | LIU Chenxu, YU Guilan |
---|---|
Format: | article |
Langue: | ZH |
Publié: |
Editorial Office of Journal of Shanghai Jiao Tong University
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/e3147e33e4744f91a5d9666d1ddbd668 |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Fluid-Structure Interaction Calculation Framework for Non-Rigid Airship Based on Explicit Dynamics
par: ZHANG Yu, et autres
Publié: (2021) -
Full-scale self-propulsion simulation with a discretized propeller
par: Zhang Qingshan, et autres
Publié: (2021) -
Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication
par: Yufei Liu, et autres
Publié: (2021) -
Data Splitting Method of Distance Metric Learning Based on Gaussian Mixed Model
par: ZHENG Dezhong, et autres
Publié: (2021) -
Network Intrusion Detection Based on Extended RBF Neural Network With Offline Reinforcement Learning
par: Manuel Lopez-Martin, et autres
Publié: (2021)