基于PSO-BP的红外温度传感器环境温度补偿

针对红外温度传感器的测温精度易受环境温度的影响，提出了一种基于粒子群优化（ PSO）GP （ PSO-GP）神经网络的温度补偿方法。所提出的PSO-GP算法解决了一般GP算法迭代速度慢、且易陷入局部最优的问题，以热电堆传感器的环境温度补偿为例，进行算法实现，并以单纯的GP算法做对照。仿真结果表明：提出的PSO-GP算法对环境温度有明显的补偿效果，且优于单纯的GP算法补偿。所构建的补偿模型具有较高的补偿精度和实用价值。

Ambient temperature compensation of infrared temperature sensor based on PSO-BP

Aiming at precision of infrared temperature sensor is affected by ambient temperature,a new method is put forward for temperature compensation based on particle swarm optimize( PSO)GP( PSO-GP)neural network. The proposed PSO-GP algorithm solves problem that iteration speed of general GP algorithm is slow,and prone to local optimum,take ambient temperature compensation of thermopile sensor for example to realize algorithm,and compare with pure GP algorithm. Simulation results show that the proposed PSO-GP algorithm has obvious compensation effect on ambient temperature,and the effect is better than that of pure GP algorithm. The constructed compensation model has high compensation precision and practical value.