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 E-mail:glyu@bjtu.edu.cn

Abstract:

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: