| 基于改进麻雀搜索算法优化BP神经网络的PM_(2.5)浓度预测北大核心CSCD |
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| 引用本文： | 赵侃,师芸,牛敏杰,王虎勤.基于改进麻雀搜索算法优化BP神经网络的PM_(2.5)浓度预测北大核心CSCD[J].测绘通报,2022,0(10):44-48. |
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| 作者姓名： | 赵侃 师芸 牛敏杰 王虎勤 |
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| 作者单位： | 1. 中煤地航测遥感局, 陕西 西安 710199;2. 西安科技大学测绘科学与技术学院, 陕西 西安 710054;3. 自然资源部煤炭资源勘察与综合利用重点实验室, 陕西 西安 710021;4. 西安捷达测控有限公司, 陕西 西安 710054 |
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| 基金项目： | 国家自然科学基金(41674013;41874012) |
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| 摘 要： | 针对传统BP神经网络模型收敛速度慢、易陷入局部极值等问题,本文采用分段线性混沌映射(PWLCM)和萤火虫算法(FA)改进麻雀搜索算法(SSA),并优化BP神经网络模型初始权值和阈值,对西安市PM_(2.5)浓度进行预测。通过比较不同模型预测结果的评价指标,并与性能较优的SSA-BP模型对比,ISSA-BP模型预测结果的RMSE、MAPE、MAE分别下降了3.70、3.73、3.34。试验结果表明,改进后的麻雀搜索算法具有高效的全局最优搜索能力,优化后的ISSA-BP神经网络预测稳定性高,精度优于BP、SSA-BP神经网络模型,可用于预测PM_(2.5)浓度。
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| 关 键 词： | 麻雀搜索算法 分段线性混沌映射 萤火虫算法 BP神经网络 PM_(2.5)浓度预测 |
| 收稿时间： | 2021-09-21 |
Prediction of PM2.5 concentration based on optimized BP neural network with improved sparrow search algorithm |
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| ZHAO Kan,SHI Yun,NIU Minjie,WANG Huqin.Prediction of PM2.5 concentration based on optimized BP neural network with improved sparrow search algorithm[J].Bulletin of Surveying and Mapping,2022,0(10):44-48. |
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| Authors: | ZHAO Kan SHI Yun NIU Minjie WANG Huqin |
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| Institution: | 1. Aerial Photogrammetry and Remote Sensing Bureau, Xi'an 710199, China;2. College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China;3. Key Laboratory of Coal Resources Exploration and Comprehensive Utilization, Ministry of Land and Resources, Xi'an 710021, China;4. Xi'an Jieda Measurement and Control Co., Ltd., Xi'an 710054, China |
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| Abstract: | Aiming at the problems of slow convergence speed and easy to fall into local extremum of traditional BP neural network model, this paper uses PWLCM(piece wise linear chaotic map)and FA(firefly algorithm)to improve SSA(sparrow search algorithm)and optimize the initial weights and thresholds of the BP neural network model to predict the PM2.5 concentration in Xi'an. By comparing the evaluation indicators of the prediction results of different models, compared with the better-performing SSA-BP model, the RMSE, MAPE and MAE of the ISSA-BP model prediction results decrease by 3.70, 3.73 and 3.34, respectively. The experimental results show that the improved sparrow search algorithm has efficient global optimal search ability. The optimized ISSA-BP neural network has high prediction stability and accuracy, which is better than BP and SSA-BP neural network models and can be used to predict PM2.5 concentration. |
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| Keywords: | sparrow search algorithm piece wise linear chaotic map firefly algorithm BP neural network PM2 5 concentration prediction |
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