基于OBF神经网络的温度传感器非线性补偿方法  

Approach to Nonlinear Compensation of Temperature Transducer Based on OBF Neural Network

作　　者：谢文力[1] 曾喆昭[2] 

机构地区：[1]湘潭大学光电材料与物理学院,湖南湘潭411105 [2]长沙理工大学电气与信息工程学院,湖南长沙410076

出　　处：《现代电子技术》2009年第13期199-201,共3页Modern Electronics Technique

基　　金：湘潭大学教改项目(1029-2904011)

摘　　要：针对热敏电阻温度传感器应用中存在的非线性问题,提出应用递推最小二乘法训练正交基(OBF)神经网络进行非线性补偿的方法。研究结果表明,与RBF神经网络非线性补偿模型和BP神经网络非线性补偿模型相比,该正交基神经网络非线性补偿模型具有误差小,精度高,训练次数少的优点,故为一种有效的非线性补偿方法,在测控领域具有实用价值。This paper presents a method to compensate nonlinear of thermistor temperature transducer using Recursive Least Square（RLS） algorithm to training nonlinear compensation model based on Orthogonal Basis Function（OBF） neural network,according to the nonlinear problem existed in the thermistor temperature transducer. The results show that the proposed method has high precision and fast network training speed compared with the compensation methods based on RBF neural network model and BP neural network model. The nonlinear compensation approach is effective and practical in measurement and control system.

关 键 词：热敏电阻 传感器 非线性补偿 OBF神经网络 

分 类 号：TP212.12[自动化与计算机技术—检测技术与自动化装置]