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| 基于卷积神经网络的轮胎花纹噪声值预测 |
| Prediction of Tire Pattern Noise Value Based on Convolutional Neural Network |
| Received:March 22, 2023 Revised:March 22, 2023 |
| DOI：10.12135/j.issn.1006-8171.2023.12.0762 |
| 中文关键词: 轮胎花纹；图像处理；卷积神经网络；BP神经网络；噪声值预测 |
| 英文关键词: tire pattern；image processing；CNN；BP neural network；noise value prediction |
| 基金项目: |
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| Author Name | Affiliation | E-mail | | lizhiwei | School of Technology, Beijing Forestry University | 13463312484@163.com | | suyu | Anhui Lupital Iot Co., Ltd | | | zhangshun | Anhui Lupital Iot Co., Ltd | | | wangqingchun* | School of Technology, Beijing Forestry University | wangqingchun@bjfu.edu.cn |
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| 中文摘要: |
| 利用图像处理和卷积神经网络（CNN）搭建轮胎花纹结构与轮胎花纹噪声值之间的数学模型，分别采用CNN模型和BP神经网络对轮胎花纹噪声值进行预测，并对比预测精度。结果表明：采用CNN模型，轮胎花纹噪声值的预测值与实测值的平均绝对误差为0.591 dB，平均相对误差为0.81%；采用BP神经网络，轮胎花纹噪声值的预测值与实测值的平均绝对误差为0.713 dB，平均相对误差为0.95%；相较于BP神经网络，CNN模型对轮胎花纹噪声值的预测精度更高。 |
| 英文摘要: |
| The image processing and convolutional neural networks（CNN）were used to establish a mathematical model between the tire pattern structure and the tire pattern noise values，the CNN model and BP neural network were used to predict the tire pattern noise values respectively，and the prediction accuracy was compared. The results showed that，using the CNN model，the average absolute error between the predicted and measured tire pattern noise values was 0.591 dB，and the average relative error was 0.81%.Using the BP neural network ，the average absolute error between the predicted value and the measured tire pattern noise value was 0.713 dB，and the average relative error was 0.95%.Compared with the BP neural networks，the CNN models had a higher prediction accuracy for tire pattern noise values. |
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