基于神经网络的层状周期结构能量传输谱预测

doi:10.16183/j.cnki.jsjtu.2019.242

上海交通大学学报 ›› 2021, Vol. 55 ›› Issue (1): 88-95.doi: 10.16183/j.cnki.jsjtu.2019.242

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

基于神经网络的层状周期结构能量传输谱预测

刘陈续, 于桂兰()

北京交通大学　土木建筑工程学院，北京　100044

收稿日期:2019-08-19 出版日期:2021-01-01 发布日期:2021-01-19
通讯作者: 于桂兰 E-mail:glyu@bjtu.edu.cn
作者简介:刘陈续(1995-)，男，江苏省徐州市人，博士生，主要从事周期结构减振隔震的研究．
基金资助:
国家自然科学基金资助项目(11772040)

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

LIU Chenxu, YU Guilan()

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

摘要/Abstract

摘要：

本文对层状周期结构的能量传输谱预测方法进行了研究．在考虑几何参数、物理参数单独变化以及同时变化3种情况下，通过构建深层反向传播(BP)神经网络，实现层状周期结构能量传输谱的精准预测．与径向基函数(RBF)神经网络进行对比实验，实验结果验证了所提方法的有效性．

关键词: 层状周期结构, 深层反向传播神经网络, 径向基函数神经网络, 能量传输谱, 衰减域

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

中图分类号:

TU591

刘陈续, 于桂兰. 基于神经网络的层状周期结构能量传输谱预测[J]. 上海交通大学学报, 2021, 55(1): 88-95.

LIU Chenxu, YU Guilan. Prediction of Energy Transmission Spectrum of Layered Periodic Structures by Neural Networks[J]. Journal of Shanghai Jiao Tong University, 2021, 55(1): 88-95.

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基于神经网络的层状周期结构能量传输谱预测

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

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