基于神经网络模型的高架轨道噪声烦恼度预测  

Prediction of Annoyance Degree of Elevated Track Noise Based on Neural Network Model

作　　者：何家敏 周俊召 罗雁云 HE Jiamin;ZHOU Junzhao;LUO Yanyun(Institute of Railway and Urban Rail Transit,Tongji University,Shanghai 201804,China;Shanghai Key Laboratory of Rail Transit Structure Durability and System Safety,Tongji University,Shanghai 201804,China)

机构地区：[1]同济大学铁道与城市轨道交通研究院,上海201804 [2]同济大学上海市轨道交通结构耐久与系统安全重点实验室,上海201804

出　　处：《噪声与振动控制》2023年第1期227-231,250,共6页Noise and Vibration Control

摘　　要：为了准确高效预测高架轨道噪声居民主观烦恼度,建立BP神经网络模型并对其进行优化,并对BP神经网络模型的训练和检验结果进行相关性分析,结果表明该模型具有很好泛化能力和学习能力。相较于多元线性回归,BP神经网络模型更适合应用于高架轨道交通噪声的烦恼度预测。将建立的烦恼度预测模型与通过仿真得到的噪声数据结合,可以为高架轨道交通噪声居民主观烦恼度的评估提供新的方法。In order to accurately and efficiently predict the subjective annoyance of residents due to elevated track noise, the BP neural network model is established and improved, and the correlation analysis of the training and test results of the BP neural network model is carried out. The results show that the model has a good generalization ability and learning ability. Compared with multiple linear regression, the BP neural network model is more suitable for the annoyance prediction of viaduct rail traffic noise. Combining the proposed annoyance prediction model with the noise data obtained by simulation can provide more intuitive information for rail transit planning or construction, and is suitable for green transportation development planning.

关 键 词：振动与波 高架轨道交通 烦恼度 预测 神经网络 

分 类 号：TB533[理学—物理]