Estimation of Acceleration Amplitude of Vehicle by Back Propagation Neural Networks

Q2 Physics and Astronomy

Advances in Acoustics and Vibration Pub Date : 2013-06-04 DOI:10.1155/2013/614025

M. Heidari, H. Homaei

{"title":"Estimation of Acceleration Amplitude of Vehicle by Back Propagation Neural Networks","authors":"M. Heidari, H. Homaei","doi":"10.1155/2013/614025","DOIUrl":null,"url":null,"abstract":"This paper investigates the variation of vertical vibrations of vehicles using a neural network (NN). The NN is a back propagation NN, which is employed to predict the amplitude of acceleration for different road conditions such as concrete, waved stone block paved, and country roads. In this paper, four supervised functions, namely, newff, newcf, newelm, and newfftd, have been used for modeling the vehicle vibrations. The networks have four inputs of velocity (), damping ratio (), natural frequency of vehicle shock absorber (), and road condition (R.C) as the independent variables and one output of acceleration amplitude (AA). Numerical data, employed for training the networks and capabilities of the models in predicting the vehicle vibrations, have been verified. Some training algorithms are used for creating the network. The results show that the Levenberg-Marquardt training algorithm and newelm function are better than other training algorithms and functions. This method is conceptually straightforward, and it is also applicable to other type vehicles for practical purposes.","PeriodicalId":44068,"journal":{"name":"Advances in Acoustics and Vibration","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2013/614025","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Acoustics and Vibration","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1155/2013/614025","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Physics and Astronomy","Score":null,"Total":0}

引用次数: 7

Abstract

This paper investigates the variation of vertical vibrations of vehicles using a neural network (NN). The NN is a back propagation NN, which is employed to predict the amplitude of acceleration for different road conditions such as concrete, waved stone block paved, and country roads. In this paper, four supervised functions, namely, newff, newcf, newelm, and newfftd, have been used for modeling the vehicle vibrations. The networks have four inputs of velocity (), damping ratio (), natural frequency of vehicle shock absorber (), and road condition (R.C) as the independent variables and one output of acceleration amplitude (AA). Numerical data, employed for training the networks and capabilities of the models in predicting the vehicle vibrations, have been verified. Some training algorithms are used for creating the network. The results show that the Levenberg-Marquardt training algorithm and newelm function are better than other training algorithms and functions. This method is conceptually straightforward, and it is also applicable to other type vehicles for practical purposes.

查看原文本刊更多论文

基于反向传播神经网络的车辆加速度幅值估计

本文利用神经网络研究了车辆垂直振动的变化规律。该神经网络是一种反向传播神经网络，用于预测不同道路条件下的加速度幅值，如混凝土、波浪石块铺砌和乡村道路。本文采用newff、newcf、newelm和newfftd四个监督函数对车辆振动进行建模。该网络以速度()、阻尼比()、汽车减振器固有频率()和路况(R.C)四个输入为自变量，以加速度幅值(AA)为一个输出。数值数据用于训练网络和模型预测车辆振动的能力，并进行了验证。一些训练算法用于创建网络。结果表明，Levenberg-Marquardt训练算法和newelm函数优于其他训练算法和函数。该方法在概念上简单明了，在实际应用中也适用于其他类型的车辆。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Advances in Acoustics and Vibration ACOUSTICS-

自引率

0.00%

发文量

期刊介绍： The aim of Advances in Acoustics and Vibration is to act as a platform for dissemination of innovative and original research and development work in the area of acoustics and vibration. The target audience of the journal comprises both researchers and practitioners. Articles with innovative works of theoretical and/or experimental nature with research and/or application focus can be considered for publication in the journal. Articles submitted for publication in Advances in Acoustics and Vibration must neither have been published previously nor be under consideration elsewhere. Subject areas include (but are not limited to): Active, semi-active, passive and combined active-passive noise and vibration control Acoustic signal processing Aero-acoustics and aviation noise Architectural acoustics Audio acoustics, mechanisms of human hearing, musical acoustics Community and environmental acoustics and vibration Computational acoustics, numerical techniques Condition monitoring, health diagnostics, vibration testing, non-destructive testing Human response to sound and vibration, Occupational noise exposure and control Industrial, machinery, transportation noise and vibration Low, mid, and high frequency noise and vibration Materials for noise and vibration control Measurement and actuation techniques, sensors, actuators Modal analysis, statistical energy analysis, wavelet analysis, inverse methods Non-linear acoustics and vibration Sound and vibration sources, source localisation, sound propagation Underwater and ship acoustics Vibro-acoustics and shock.