Virtual Realization of Temperature Measurement of PRTD Based on Radial Basis Function Neural Network
The problem of estimating the sensors input-output characteristic is being increasingly tackled using software techniques.Artificial neural network (ANN)method is a useful curve-fitting method.A model of PRTD (Platinum Resistance-temperature Device)using radial basis function (RBF)neural network is presented.After the successful training of ANN using scale division data,it is then used as a neural estimator to calculate the temperature from resistance.Suitable spread parameter of the model is determined as 0.3~1.0.The absolute error is 0 for data in specimen and 0.03~0.06 out of specimen.The results indicate that model presented is simple in algorithm,accurate and steady.Software and hardware composition of VI system and virtual realization process of temperature measurement based on joint programming of LabVIEW and MATLAB including front panel and block diagram are given.Running results indicate that virtual realization method integrating ANN and VI is easy and inexpensive and can be used in other application fields for reference.
RBF neural network thermal resistor LabVIEW MATLAB virtual instrument
Xia Changhao Liu Yong
China Three Gorges University,Yichang,Hubei,443002 China
国际会议
西安
英文
2007-08-16（万方平台首次上网日期，不代表论文的发表时间）