Journal of Shanghai Jiao Tong University ›› 2021, Vol. 55 ›› Issue (1): 88-95.doi: 10.16183/j.cnki.jsjtu.2019.242

Special Issue: 《上海交通大学学报》2021年“土木建筑工程”专题 《上海交通大学学报》2021年12期专题汇总专辑

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Prediction of Energy Transmission Spectrum of Layered Periodic Structures by Neural Networks

LIU Chenxu, YU Guilan()   

  1. School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China
  • Received:2019-08-19 Online:2021-01-01 Published:2021-01-19
  • Contact: YU Guilan


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.

Key words: layered periodic structure, deep back propagation (BP) neural network, radial basis function (RBF) neural network, energy transmission spectrum, attenuation domain

CLC Number: