Data fusion technology in vehicle traction performance test based on RBF neural network

SU Ying1; CHEN Ping1

doi:10.13873/j.1000-97872007.02.014

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Data fusion technology in vehicle traction performance test based on RBF neural network

更新时间：2025-09-11

- Data fusion technology in vehicle traction performance test based on RBF neural network
- Issue 2, Pages: 43-44+47(2007)
- 作者机构：
  
  1. 山东理工大学计算机科学与技术学院
  2. 山东理工大学计算机科学与技术学院,山东,淄博,255049
  3. 滨州学院计算机科学技术系
  4. ,山东,滨州,256600
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872007.02.014
  CLC： TP274.4;TP183
- Published：2007
- 稿件说明：
移动端阅览
[1]苏英,陈平.基于RBF神经网络的车辆牵引性能测试数据融合技术[J].传感器与微系统,2007(02):43-44+47.

SU Ying1, CHEN Ping1. Data fusion technology in vehicle traction performance test based on RBF neural network[J]. 2007, (2): 43-44+47.
[1]苏英,陈平.基于RBF神经网络的车辆牵引性能测试数据融合技术[J].传感器与微系统,2007(02):43-44+47. DOI： 10.13873/j.1000-97872007.02.014.

SU Ying1, CHEN Ping1. Data fusion technology in vehicle traction performance test based on RBF neural network[J]. 2007, (2): 43-44+47. DOI： 10.13873/j.1000-97872007.02.014.

摘要

针对目前车辆牵引性能测试中

牵引力传感器由于环境温度变化出现测量误差

影响了测试精度的问题

采用基于神经网络的数据融合技术对其进行补偿

不但避免了硬件补偿的复杂性

且提高了测试精度

取得较好的效果。实验证明:采用基于径向基函数（RBF）神经网络的数据融合技术补偿牵引力传感器中由于温度漂移而引起的误差较传统的补偿方式

具有较大的优势

有一定的实用性和推广价值。

Abstract

In vehicle traction performance test

the traction sensor’s test has great error because of the change of the condition temperature

so the data fusion technology based on RBF neural network is adoptd to compensate the error.This method reduces the compensation complexion and improves the accuracy.Relative to other conventional methods

it has more great advantages and is practicable.

关键词

Keywords

references

基于LabVIEW和神经网络的压力传感器温度补偿系统 [J]. 高迎慧,王文,薛永存. 仪器仪表用户 . 2006(01)

基于BP神经网络的数据融合方法 [J]. 沈飞,郭军. 自动化与仪器仪表 . 2005(05)

基于遗传算法的RBF神经网络在热敏电阻温度传感器非线性补偿中的应用 [J]. 俞阿龙. 电工技术学报 . 2005(08)

基于多传感器的数据融合技术 [J]. 胡丹丹,肖书明,王燕清,高庆吉. 东北电力学院学报 . 2004(01)

基于先进数据处理技术的智能称重传感器研究 [D]. 朱子健. 南京航空航天大学 2005

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