基于遗传神经网络的多光谱辐射测温法

针对BP神经网络易陷入局部极小等缺陷，将遗传算法(GA)与神经网络相结合，提出了一种将GA-BP算法应用于多光谱辐射测温的数据处理方法，并对基于亮度温度模型的多光谱辐射测温数据进行了仿真实验。结果表明：已训练样本的真实温度识别精度，GA-BP算法为±5 K，BP神经网络为±10 K;未训练样本的真实温度识别精度，GA-BP算法为±10 K，BP神经网络为±20 K;无论是GA-BP算法还是BP神经网络，已训练样本的真实温度识别精度比未训练样本的真实温度识别精度都更精确些，靠近训练样本集边缘的样本真实温度的识别精度偏低。说明GA-BP算法比BP神经网络可以更好地解决了目标真实温度的测量问题。

Multi-Spectral Thermometry Based on GA-BP Algorithm

Considering some defects of back-propagation neural network (BP), a new algorithm combining genetic algorithm (GA) with BP was described. The application of GA-BP to the data processing of multi-spectral thermometry was proposed. The simulation experiments, based on GA-BP algorithm and BP neural network respectively, show that the recognition precision of trained emissivity samples is +/-5 K and +/-10 K respectively, and that of untrained emissivity samples is +/-10 K and +/-20 K respectively. No matter GA-BP algorithm or BP neural network is used, in general, the recognition precision of trained emissivity samples is higher than that of untrained emissivity samples. The recognition precision of true temperature is lower near the edge of sample sets. The GA-BP algorithm was more efficient than the BP neural network in the true temperature measurement.