摘要
提出了一种基于RBF神经网络的数据融合方法,并用此方法对电阻应变式压力传感器进行了温度补偿。通过一个实例说明了该方法的应用,并与利用BP神经网络进行补偿的方法进行了比较,进一步说明了该方法的优越性。结果表明当环境温度变化较大时,在不同的压力下该方法能对传感器进行有效的温度补偿。
A data fusion method based on RBF neural network is proposed, based on which temperature compensation for the resistance pressure transducer is implemented. A case study illustrates its application procedures. The advantage of this method is indicated by comparing with that of BP neural network The results show its effectiveness for different pressure inputs in the case of limited environment temperature variation.
出处
《华北电力大学学报（自然科学版）》
CAS
北大核心
2003年第2期64-67,共4页
Journal of North China Electric Power University：Natural Science Edition
关键词
数据融合
RBF神经网络
温度补偿
data fusion
RBF neural network
temperature compensation
