| 申秀敏,左曙光,何吕昌,张世炜,李林.车内噪声声品质的神经网络预测[J].声学技术,2009,(3):264~268 |
| 车内噪声声品质的神经网络预测 |
| BP neural network prediction of car interior sound quality |
| 投稿时间：2009-01-04 修订日期：2009-03-14 |
| DOI： |
| 中文关键词: 车内噪声 声品质 BP神经网络 预测模型 |
| 英文关键词: fuel cell vehicle sound quality BP neural network predict model |
| 基金项目:国家863计划电动汽车重大专项(2005AA501000);上海市曙光计划项目(05SG22);汉高公司资助 |
| 作者 | 单位 | | 申秀敏 | 同济大学新能源汽车工程中心, 上海, 201804 | | 左曙光 | 同济大学新能源汽车工程中心, 上海, 201804 | | 何吕昌 | 同济大学新能源汽车工程中心, 上海, 201804 | | 张世炜 | 同济大学新能源汽车工程中心, 上海, 201804 | | 李林 | 同济大学新能源汽车工程中心, 上海, 201804 |
|
| 摘要点击次数: 1235 |
| 全文下载次数: 1287 |
| 中文摘要: |
| 鉴于车内噪声声品质评价的复杂性和非线性的特征,分析了BP神经网络方法在车内噪声声品质预测中的应用,阐述了其基本原理和模型并结合实例提出了完整的实施流程。该预测方法具有很强的学习能力,各连接权重由网络通过学习自主生成,因此预测结果更具客观性和准确性。同时将用此种方法与现有的预测方法得出的结果进行比较,得出结论:神经网络用于车内噪声主客观评价数据处理可以得到更好的预测效果,从而在很大程度上提高评价者的决策水平,对现代汽车噪声的评价、分析与控制都具有重要意义。 |
| 英文摘要: |
| Based on the complexity and non-linear characteristics of car interior sound quality evaluation,the problem about the application of BP neural network in car interior sound quality prediction is discussed.Combining the basic principles and models with examples the complete implementation process is presented.The self-learning ability of this prediction method is so strong that each connection weight could generate itself via network learning with a more objective and accurate predicted result.Then BP neural network evaluation method is compared with the existing prediction methods,and the conclusion shows that in interior vehicle subjective and objective noise evaluation,the prediction effect of the former method is much better.To a large extent the method could be used to improve the decision-making accuracy,which is of great importance to the evaluation,analysis and control of automotive noise nowadays. |
|
查看全文
查看/发表评论 下载PDF阅读器 |
| 关闭 |
|
|
|
