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
曹旭 1张舜 2许彦峰 1王青春1
作者信息
- 1. 北京林业大学工学院,北京 100083
- 2. 安徽路必达智能科技有限公司,安徽合肥 230031
- 折叠
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％.
Abstract
The tire load measurement method based on particle swarm optimization(PSO)algorithm-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 changing working conditions,the prediction error was 2.496％.
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Key words
tire load/tire status information/acceleration characteristic/PSO algorithm/BP neural network引用本文复制引用
出版年
2024
NETL
NSTL
万方数据