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.