| 基于径向基神经网络的压力传感器信息融合技术 |
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| 引用本文： | 张银慧,何平,李晓峰,赵红东.基于径向基神经网络的压力传感器信息融合技术[J].河北工业大学学报,2008,37(1):22-26. |
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| 作者姓名： | 张银慧 何平 李晓峰 赵红东 |
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| 作者单位： | 1. 河北工业大学信息工程学院,天津,300401
2. 河北工业大学计算机科学与软件学院,天津,300401 |
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| 基金项目： | 河北省自然科学基金资助项目（F2007000096）;教育部高等学校博士学科点专项科研基金资助（20070080001） |
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| 摘 要： | 因为压力传感器的温度漂移普遍存在，其输出特性容易受环境温度、电压扰动等各种非目标参量的影响，从而大大降低了其性能．利用径向基神经网络构建了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，从而大大提高了压力传感器的稳定性和可靠性．
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| 关 键 词： | 径向基神经网络 压力传感器 数据融合 精度 |
| 文章编号： | 1008-2373（2008）01-0022-05 |
| 收稿时间： | 2007-04-24 |
Information Fusion of Pressure Sensor Based on RBF Network |
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| ZHANG Yin-hui,HE Ping,LI Xiao-feng,ZHAO Hong-dong.Information Fusion of Pressure Sensor Based on RBF Network[J].Journal of Hebei University of Technology,2008,37(1):22-26. |
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| Authors: | ZHANG Yin-hui HE Ping LI Xiao-feng ZHAO Hong-dong |
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| Affiliation: | ZHANG Yin-hui1,HE Ping2,LI Xiao-feng1,ZHAO Hong-dong1 ( 1. School of Information,Engineering,Hebei University of Technology,Tianjin 300401,China,2. School of Computer Science , Software,China ) |
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| Abstract: | As temperature drift exist in many pressure sensors, the output of pressure sensor is easily affected by non-objection parameters, such as environment temperatures, voltage fluctuation and so on, in the application. A network model with two inputs and single output is constructed by radial basis function neural network. High precision temperature compensation of pressure sensor is achieved by gradient descend algorithm with a momentum factor in this network model. The result is that the stability and liability of the pressure sensors are improved. |
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| Keywords: | RBF network pressure sensor data fusion precision |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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