In order to improve the prediction accuracy of the internal temperature of the high and low temperature test chamber,the BP neural network(PSO-BP)model optimized by the particle swarm optimization algorithm is established to predict the temperature change in the working area of the high and low temperature test chamber,and the finite point temperature data collected by the test is used for comparative analysis,which provides theoretical and data support for the analysis and calculation of the temperature characteristics in the high and low temperature test chamber.The results show that the minimum training error of PSO-BP network is 9.35×10-5.Compared with BP neural network,the fitting accuracy of training set and test set of optimized PSO-BP neural network is improved by 1.09% and 2.43% respectively.The mean absolute error(MAE)of BP network and PSO-BP network were 1.480 and 0.753,respectively,and the root mean square error(RMSE)were 1.979 and 1.842,respectively.The results show that the PSO-BP neural network has higher prediction accuracy,which can effectively obtain the continuous and complete temperature situation in the high and low temperature test chamber,and improve the research and development efficiency of the test chamber.
high and low temperature test chamberparticle swarm optimization algorithmBP neural networktemperature prediction
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万方数据
