A new method for constructing spectral emissivity models for measuring the real temperature of targets

The main problem in radiation pyrometry is the large error arising from the unknown or varying emissivity of the target surface. In this paper, a combined neural network emissivity model, which allows calculating the true temperature and emissivity of any measurand from the measured data of continuous spectral emissivity, is established by the combined neural network. The proposed single parameter dynamic search algorithm using the hybrid steepest descent and Newton's method is proposed to optimize the training algorithm of the model. This optimization algorithm can quicken the convergence speed of the combined neural network emissivity model (CNNE model). Through theoretical derivation and simulation experiments, we can see that this model is theoretically useful for any target, which calculates the function relationships between wavelength and emissivity. Simulation and experimental results have shown the high accuracy in measurements by using this method.