石岩,舒歌群,毕凤荣,刘海.基于神经网络的车辆排气噪声声音品质预测技术[J].农业机械学报,2010,41(8):16-19.
.Prediction of Vehicle Exhaust Noise Based on Neural Network[J].Transactions of the Chinese Society for Agricultural Machinery,2010,41(8):16-19.
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基于神经网络的车辆排气噪声声音品质预测技术   [下载全文]
Prediction of Vehicle Exhaust Noise Based on Neural Network   [Download Pdf][in English]
  
DOI:10.3969/j.issn.1000-1298.[year].[issue].[sequence]
中文关键词:  车辆  声音品质  排气噪声  神经网络  预测模型
基金项目:
石岩  舒歌群  毕凤荣  刘海
天津大学
中文摘要:通过评审团成对比较法测试得到18种车辆排气噪声的满意度评价,考察并选取响度、尖锐度、粗糙度、波动度和峭度作为描述车辆排气噪声声音品质的客观心理声学参数,使用BP神经网络理论建立车辆排气噪声声音品质神经网络预测模型,对排气噪声样本的满意度进行预测,并与使用多元线性回归模型所得的预测值进行了比较。结果表明,神经网络模型预测值更接近实测值,误差在10%范围以内,对于单一噪声样本满意度的预测精度高于多元线性回归模型,能够更好地反映客观参数和主观满意度间的非线性关系,可用于车辆排气噪声声音品质的预测研究。
Key Words:
Abstract:Sensory pleasantness evaluation of eighteen vehicle exhaust noises were obtained by paired comparison jury test. Loudness, sharpness, roughness, fluctuation strength and kurtosis were selected for objectively characterizing the sound quality of exhaust noise. The sound quality prediction model of vehicle exhaust noise was established based on back-propagation neural network. Sensory pleasantness of exhaust noise samples were obtained through the prediction model and the results were compared with that obtained through multiple linear regression prediction model. The result showed that the prediction values were close to the measured values, the neural network model was more effective than multiple linear regression model in prediction of individual exhaust noise. The neural network prediction model represented the nonlinear relation between sensory pleasantness and objective parameters exactly and could be used for predicting the sound quality of vehicle exhaust noise.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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