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| 基于PSO-BP神经网络的轮胎负荷测量方法 |
| Tire Load Measurement Method Based on PSO-BP Neural Network |
| Received:July 24, 2023 Revised:July 24, 2023 |
| DOI：10.12135/j.issn.1006-8171.2024.05.0312 |
| 中文关键词: 轮胎负荷；轮胎状态信息；加速度特征；粒子群优化算法；BP神经网络 |
| 英文关键词: tire load；tire status information；acceleration characteristic；PSO algorithm；BP neural networ |
| 基金项目: |
| 中图分类号: |
| Author Name | Affiliation | E-mail | | CAO Xu | Beijing Forestry University | 360123308@qq.com | | ZHANG Shun | Anhui Lupital Iot Co.，Ltd | | | XU Yanfeng | Beijing Forestry University | | | WANG Qingchun* | Beijing Forestry University | wangqingchun@bjfu.edu.cn |
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| 中文摘要: |
| 研究基于粒子群优化（PSO）算法-BP神经网络的轮胎负荷测量方法。将采集的轮胎状态信息与提取到的加速度特征输入到BP神经网络，对轮胎负荷进行回归预测，使用PSO算法优化BP神经网络的权值与阈值，得到轮胎状态信息与轮胎负荷的关系。结果表明，采用PSO-BP神经网络预测轮胎负荷误差为1.865 6 %，PSO-BP神经网络预测精度较高，在转变工况条件下，预测误差为2.496%。 |
| 英文摘要: |
| The tire load measurement method based on particle swarm optimization （PSO）-BP neural network was studied.The collected tire condition information and extracted acceleration features were input into the BP neural network to regressively predict the tire load.The weight and threshold of BP neural network were optimized by PSO algorithm，and the relationship between tire state information and tire load was obtained.The results showed that the prediction error for tire load using the PSO-BP neural network was 1.865 6% and the prediction accuracy of PSO-BP neural network was higher.Under the condition of changing working conditions，the prediction error was 2.496%. |
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