摘要
针对PM2.5质量浓度序列不确定性和随机性特征,提出一种基于互补集合经验模态分解和优化Elman神经网络的区间预测模型.首先,利用互补集合经验模态分解将原始PM2.5质量浓度序列进行分解,并用样本熵将其重组为复杂度差异明显的子序列.其次,针对各子序列分别用多输入单输出Elman神经网络(Elman neural network,ENN)建立PM2.5质量浓度预测模型.在各子序列预测结果基础之上,采用多输入双输出Elman神经网络实现PM2.5质量浓度区间预测.最后,为了进一步提高预测模型性能,提出一种区间预测评价指标作为目标函数,采用思维进化算法对Elman神经网络权值β和阈值b进行寻优.基于北京工业大学校园监测站点采集数据,验证了预测模型的可靠性和有效性.所提预测模型为PM2.5质量浓度预测提供了一种方法.
Considering the uncertainty and randomness of PM2.5 concentration sequence,an interval prediction model based on complementary ensemble empirical mode decomposition and optimized Elman neural network was proposed.First,the original PM2.5 concentration sequence was decomposed by using the complementary ensemble empirical mode.They were reorganized into several sub-sequences with the obvious differences in complexity by the sample entropy method.Second,a prediction model based on the multi input single output Elman neural network was established for each sub-sequence,respectively.Based on the results of each sub-sequence prediction,an interval prediction model based on the multi input double output Elman neural network was established for the prediction of PM2.5 concentration.Finally,a novel interval prediction evaluation index was introduced as the objective function to further improve the prediction performance.The weightβand the threshold b of the Elman neural network were optimized by using the mind evolution algorithm.Based on the monitoring data,which was taken from the campus of Beijing University of Technology,the proposed prediction model was verified for reliable and good interval prediction results.It can provide a method for PM2.5 concentration prediction.
作者
李晓理
梅建想
王康
李济瀚
LI Xiaoli;MEI Jianxiang;WANG Kang;LI Jihan(Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China;Beijing Key Laboratory of Computational Intelligence and Intelligent Systems,Beijing 100124,China;Engineering Research Center of Digital Community,Ministry of Education,Beijing 100124,China)
出处
《北京工业大学学报》
CAS
CSCD
北大核心
2020年第4期377-384,共8页
Journal of Beijing University of Technology
基金
国家自然科学基金资助项目(61873006,61473034,61673053)
国家重点研发计划资助项目(2018YFC1602704,2018YFB1702704)。
