基于粒子群算法优化BP神经网络的高低温试验箱温度预测  

Temperature Prediction of High and Low Temperature Test Chamber Based on BP Neural Network Optimized By Particle Swarm Optimization Algorithm

作　　者：彭白雪 陈清华[1,2] 王建刚 王皖楠 PENG Bai-xue;CHEN Qing-hua;WANG Jian-gang;WANG Wan-nan(School of Mechanical and Electrical Engineering,Anhui University of Science and Technology,Huainan 232001;Guangdong Lijia Industrial Co.,Ltd.,Dongguan 523000)

机构地区：[1]安徽理工大学机电工程学院,淮南232001 [2]广东立佳实业有限公司,东莞523000

出　　处：《环境技术》2024年第5期215-223,共9页Environmental Technology

基　　金：安徽省重点研究与开发计划项目,项目编号:2022a05020030;安徽理工大学环境友好材料与职业健康研究院研发专项基金资助项目,项目编号:ALW2021YF12

摘　　要：为提高高低温试验箱内部温度预测精度,通过建立粒子群算法优化后的BP神经网络(PSO-BP)模型对高低温试验箱内工作区温度变化情况进行预测,并利用试验采集的有限点温度数据进行对比分析,为高低温试验箱内温度特性的分析计算提供理论和数据支持。结果表明PSO-BP网络取得最小训练误差为9.35×10^(-5),与BP神经网络相比,优化后的PSO-BP神经网络训练集和测试集拟合精度分别提高了1.09%和2.43%。BP网络和PSO-BP网络平均绝对误差(MAE)分别为1.480和0.753,均方根误差(RMSE)分别为1.979和1.842,综合表明PSOBP神经网络预测精准度更高,可有效获得高低温试验箱内连续完整的温度情况,提高了试验箱研发工作效率。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 PS0-BP network is 9.35×10^(-5).Compared with BP neural network,the fitting accuracy of training set and test set of optimized PS0-BP neural network is improved by 1.09%and 2.43%respectively.The mean absolute error(MAE)of BPnetwork and PS0-BPnetworkwere 1.480and0.753,respectively,andthe rootmean square error(RMSE)were 1.979 and 1.842,respectively.The results show that the PS0-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.

关 键 词：高低温试验箱 粒子群算法 BP神经网络 温度预测 

分 类 号：TK173[动力工程及工程热物理—热能工程]