Design on Infrared Temperature Measurement Compensation Algorithm Based on PSO-BP Neural Network

Abstract

A temperature measurement compensation algorithm based on particle swarm optimization (PSO) back propagation (BP) neural network is proposed for the temperature measurement accuracy of infrared thermal imager affected by ambient temperature, measurement distance and other factors. By optimizing the initial weight and threshold of BP neural network, PSO algorithm overcomes the shortcomings of BP algorithm, such as slow convergence speed, easy to fall into local optimization and low accuracy. At the same time, the inertia weight is introduced into the PSO-BP algorithm, so that the algorithm maintains a strong global search ability and a more accurate local search ability. Compared with the single BP algorithm, the generalization ability and temperature measurement accuracy of the system are effectively improved, and the average value of the mean square error is reduced to 0.0443, which achieves the ideal effect.