| 基于RBF 神经网络提高压力传感器精度的新方法 |
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| 引用本文： | 赵望达,刘勇求,贺毅.基于RBF 神经网络提高压力传感器精度的新方法[J].传感技术学报,2004,17(4):640-642. |
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| 作者姓名： | 赵望达 刘勇求 贺毅 |
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| 作者单位： | 中南大学防灾科学与安全技术研究所,铁道校区,长沙,410075 |
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| 摘 要： | 传感器的温度漂移普遍存在,提出了一种新的补偿方法.用智能温度传感器DS18B20作为辅助传感器,结合主传感器测量变量,利用径向基函数(RBF)神经网络构建双输入单输出网络模型,采用带遗忘因子的梯度下降算法实现了压力传感器高精度温度补偿,比普通补偿方法精度提高了2～5倍.
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| 关 键 词： | 压力传感器 精度 温度补偿 径向基函数神经网络 温度传感器DS18B20 |
| 文章编号： | 1004-1699(2004)04-0640-03 |
| 修稿时间： | 2004年6月14日 |
A New Method to Improve Pressure Sensor Precision Based on RBF Neural Netw |
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| ZHAO Wang??d,LIU Yong??qiuo,HE Yi.A New Method to Improve Pressure Sensor Precision Based on RBF Neural Netw[J].Journal of Transduction Technology,2004,17(4):640-642. |
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| Authors: | ZHAO Wang??d LIU Yong??qiuo HE Yi |
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| Affiliation: | Institute of Disaster Prevention Science and Safety Technology , Central South University , Changsha 410075, China |
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| Abstract: | As temperature drift exist in many sensors, a new method of ?? sensor compensation is put forward. Intelli??
gent temperature sensor DS18B20 is adopted as auxiliary sensor. A network model with two inputs and single output is
constructed by radial basis function neural network. The two inputs include DS18B20 sensor and a main sensor. High
precision temperature compensation of pressure sensor is achieved by gradient descend algorithm with a momentum fac??
tor in this network model. Measurement precision is improved 2~ 5 times comparing with general compensation meth??
od. |
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| Keywords: | pressure sensor precision temperature compensation radial basis function neural network temperature sensor DS18B20 |
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