The application of artificial neutral network to data processing of multi-spectral radiation thermometry was presented. By taking advantages of neutral network and various emissivity samples, the emissivity models of the targets were identified and the true temperature and spectral emissivity were available simultaneously. The measurement accuracy was improved further by subdivision. The effects of measurement errors on the measurement accuracy of temperature and emissivity were also analyzed. Computer simulation results proved that the method is an effective way for both temperature and emissivity measurements.%介绍了一种人工神经网络在多光谱测温数据处理中的应用.利用人工神经网络,结合多种发射训练样本模型,可以自动辨识被测目标的发射率模型,从而得到目标的真温和光谱发射率.应用二次细分的方法进一步提高了测量精度,并分析了各种测量误差对测温精度的影响.仿真结果表明此方法是获知真温与发射率的一种较好的方法.
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