Sound quality prediction and weight analysis of vehicles based on GA-BP neural network

GAO Yin-han; TANG Rong-jiang; LIANG Jie; ZHAO Tong-hang; ZHANG Li-tong

doi:10.3788/OPE.20132102.0462

您当前的位置：

首页 >

文章列表页 >

Sound quality prediction and weight analysis of vehicles based on GA-BP neural network

Article | 更新时间：2020-08-12

- Sound quality prediction and weight analysis of vehicles based on GA-BP neural network
- Optics and Precision Engineering Vol. 21, Issue 2, Pages: 462-468(2013)
- 作者机构：
  
  吉林大学汽车仿真与控制国家重点实验室2. 吉林大学仪器科学与电气工程学院3. 一汽集团技术中心
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20132102.0462
  CLC： TP183;U467.4
- Received：24 July 2012，
  
  Revised：10 October 2012，
  
  Published Online：23 February 2013，
  
  Published：15 February 2013
- 稿件说明：
移动端阅览
高印寒唐荣江梁杰赵彤航张澧桐. 汽车声品质的GA-BP网络预测与权重分析[J]. 光学精密工程, 2013,21(2): 462-468

GAO Yin-han TANG Rong-jiang LIANG Jie ZHAO Tong-hang ZHANG Li-tong. Sound quality prediction and weight analysis of vehicles based on GA-BP neural network[J]. Editorial Office of Optics and Precision Engineering, 2013,21(2): 462-468
高印寒唐荣江梁杰赵彤航张澧桐. 汽车声品质的GA-BP网络预测与权重分析[J]. 光学精密工程, 2013,21(2): 462-468 DOI： 10.3788/OPE.20132102.0462.

GAO Yin-han TANG Rong-jiang LIANG Jie ZHAO Tong-hang ZHANG Li-tong. Sound quality prediction and weight analysis of vehicles based on GA-BP neural network[J]. Editorial Office of Optics and Precision Engineering, 2013,21(2): 462-468 DOI： 10.3788/OPE.20132102.0462.

摘要

为了高效而准确地评价与控制车内噪声品质，以B级车稳态工况下副驾位置的车内噪声为研究对象，采用等级评分法对采集到的声音样本进行了主观评价试验，同时计算了7个客观参数。以客观参量为输入，声品质主观结果为输出，引入基于遗传算法的BP神经网络建立了声品质预测模型。实验显示该模型输出结果与实际评分的相关系数达到0.928，检验组的预测最大误差为±8%。以所建模型的连接权值，分析了客观参数对主观评价结果的贡献度，并以影响系数较大的参数为输入重新构建了预测模型。研究结果表明：稳态工况下，车内声品质主要受响度、粗糙度和尖锐度的影响，其预测模型可由这3个参数来描述。

Abstract

This paper carried out a subjective evaluation test with magnitude estimation for 78 noise samples to evaluate the sound quality of vehicles. In the test

six types of B-Class vehicles were taken as the study objects and sound signals collected in co-driver locations at steady states as experimental samples. Meanwhile

seven objective parameters were calculated to describe the sound characteristics. By using objective parameters as inputs

subjective values as outputs

a GA-BP neural network was adopted to establish a sound quality prediction model. Experiments show that the model gives good predictions of high correlation (0.928) and low error (±8%). Then

the network connection coefficients were used to calculate the impact weight of objective parameters on the results of subjective evaluation

and a new model with main parameters was established. As expected

the loudness

sharpness and roughness with a total relative importance of 83% are the most influential parameters in vehicle interior sound quality.

关键词

Keywords

references

GENUIT K. The sound quality of vehicle interior noise: a challenge for the NVH engineers [J]. Vehicle Noise and Vibration, 2004, 1(1):158-168. [2]SANG-KWON L. Objective evaluation of interior sound quality in passenger cars during acceleration [J]. Journal of Sound and Vibration, 2008, 310(5):149-168.[3]高印寒,孙强,梁杰.B级轿车车内噪声品质的主观评价研究[J].噪声与振动控制,2010,30(4):115-118.GAO YH, SUN Q, LIANG J. Subjective evaluation research of B·class car’s interior sound quality [J]. Noise and Vibration Control, 2010, 30(4):115-118. (in Chinese)[4]申秀敏,左曙光,李林,等.车内噪声声品质的支持向量机预测[J].振动与冲击,2010,29(6):66-68.SHEN X M, ZUO S G, LI L, et al.. SVM predictor of vehicle interior sound quality [J]. Journal of Vibration and Shock, 2010, 29(6):66-68. (in Chinese)[5]SAHIN Y, IKBALK E. Sound quality analysis of cars using hybrid neural networks[J]. Simulation Modeling Practice and Theory, 2008, 16(4):410-418.[6]孙强.基于人工神经网络的汽车声品质评价与应用研究[D].长春：吉林大学,2011:50-53.SUN Q. Research on evaluation and application of vehicle sound quality based on artificial neural network[D]. Changchun:Jilin University, 2011: 50-53. (in Chinese)[7]董长虹.Matlab神经网络与应用[M].北京:国防工业出版社工程,2005:89-93.DONG C H. Matlab Neural Network and Application[M]. Beijing:National Defense Industry Press, 2005:89-93. (in Chinese)[8]郭海湘,诸克军,胡杰.GA-BP 嵌套算法的理论及应用[J].数学的实践与认识,2008,38(1):116-125.GUO H X, ZHU K J, HU J. The theory and application of GA-BP-NESTING algorithm [J]. Mathematics in Practice and Theory, 2008, 38 (1):116-125. (in Chinese)[9]王平,王彩芸,王文健. GA-BP网络在钢轨磨损量预测中的应用[J].润滑与密封,2011,36(2):99-102.WANG P, WANG C Y, WANG W J. Application of GA-BP ANN in the prediction of wear volumes of rail steel [J]. Lubrication Engineering, 2011, 36(2):99-102. (in Chinese)[10]MENG X P, ZHANG H G, TAN W Y.A hybrid method of GA and BP for short-term economic dispatch of hydrothermal power systems [J]. Mathematics and Computers in Simulation,2000,51(4):341-348. [11]ALEBOYEH A, KASIRI M B. Prediction of azo dye decolorization by UV/H2O2 using artificial neural networks[J]. Dyes and Pigments, 2008(77):288-294.

Views

227

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰