Prediction of absolute Seebeck coefficients at ITS-90 temperatures using an artificial neural network

In view of the defects in model of thermocouple characteristic using BP neural network (BPNN), such as lower precision, varying output, instability( after repeated training, the output may be queer), a model of thermocouple characteristic based on Generalized Regression Neural Network (GRNN) is established. The paper gives the process of model building for Ni-CrConstantan thermocouple characteristic within -270 ～ 1000℃. By means of network training, simulation and error analysis, the scope of spread parameter of neural network model for Ni-Cr Constantan thermocouple was found. When the spread is at 0.01~1.5, bigger errors appear mainly when thermo-EMF is less than 0V and greater than 75V. When it is at 0~0.01, the model has high precision and absolute error between simulation temperature and setting temperature is close to 0℃ (the mean squared error is 0.00000703~0 ℃ ). The results indicate that the model presented has a quick convergent speed in learning process, a higher accuracy and stability within a certain parameter scope. If the model is stored in CPU of an intelligent instrument, the instrument will have high accuracy without increasing hardware cost.

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Prediction of absolute Seebeck coefficients at ITS-90 temperatures using an artificial neural network

Cited by 3 publications

References 25 publications

AI-driven ensemble learning for accurate Seebeck coefficient prediction in half-Heusler compounds based on chemical formulas

AI-driven ensemble learning for accurate Seebeck coefficient prediction in half-Heusler compounds based on chemical formulas

Virtual Realization of Temperature Measurement of PRTD Based on Radial Basis Function Neural Network

Research on a Generalized Regression Neural Network Model of Thermocouple and it's Spread Scope

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