[1]王 健,宋 颖,吴 涛.基于 LSTM 网络与误差补偿的预测模型[J].计算机技术与发展,2023,33(03):133-138.[doi:10. 3969 / j. issn. 1673-629X. 2023. 03. 020]
　WANG Jian,SONG Ying,WU Tao.Prediction Model Based on LSTM Network and Error Compensation[J].,2023,33(03):133-138.[doi:10. 3969 / j. issn. 1673-629X. 2023. 03. 020]
点击复制

基于 LSTM 网络与误差补偿的预测模型()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
33
期数:
2023年03期
页码:
133-138
栏目:
人工智能
出版日期:
2023-03-10

文章信息/Info

Title:
Prediction Model Based on LSTM Network and Error Compensation
文章编号:
1673-629X(2023)03-0133-06
作者:
王 健1; 2 ; 宋 颖1; 2 ; 吴 涛1; 2
1. 安徽大学 数学科学学院,安徽 合肥 230031;
2. 安徽大学 计算智能与信号处理教育部重点实验室,安徽 合肥 230039
Author(s):
WANG Jian1; 2 ; SONG Ying1; 2 ; WU Tao1; 2
1. School of Mathematical Sciences,Anhui University,Hefei 230031,China;
2. Key Lab of Intelligent Computing and Signal Processing of Ministry of Education,Hefei 230039,China
关键词:
PM2. 5 预测; 长短时记忆网络; 模糊聚类; 误差相似性; 误差补偿
Keywords:
PM2. 5 prediction; LSTM; fuzzy clustering; error similarity; error compensation
分类号:
TP183
DOI:
10. 3969 / j. issn. 1673-629X. 2023. 03. 020
摘要:
随着时代发展,空气质量逐渐受到人们的重视,所以对未来空气污染物的变化预测显得尤为重要。 首先,针对PM2. 5 的非线性变化以及变化所具有的周期性,选取完整年度数据进行训练和预测,使用对非线性序列数据拟合效果较好的 LSTM 网络作为初步预测模型,选择合适的滑动窗口,使用训练数据,建立了 LSTM 网络预测模型。 由于 LSTM 网络预测结果中存在相邻年份误差分布相似,但整年分布不均匀的现象,使用 FCM 对训练数据及误差进行模糊聚类。 通过聚类中心,对当前预测数据进行分类,并利用聚类结果,得到当前预测数据的误差补偿值,对 LSTM 网络的当前预测结果进行误差补偿,得到最终预测结果。 最后,通过合肥 2017 年至 2021 年的空气污染数据对该方法进行了验证,结果表明,所建模型的效果优于其他对比模型,具有一定的可行性与有效性。
Abstract:
With the development of the times,people pay more and more attention to air quality,so it is particularly important to predictthe change of air pollution in the future. Firstly,aiming at the nonlinear change and periodicity of PM2. 5 ,select the compete annual datafor training and prediction. LSTM network,which has excellent fitting effect on the nonlinear sequence data,is used as the preliminaryprediction model. The LSTM network prediction model is established by selecting the appropriate sliding window and using the trainingdata. For the phenomenon of similar error distribution in adjacent years but uneven distribution in the whole year in the prediction results　of LSTM network,the training data and errors are fuzzy clustered by FCM. Through the clustering results,the current prediction data areclassified,and the error compensation value of the current prediction data is obtained by using the clustering center. Using the error compensation value to modify the prediction result of LSTM network,the final prediction result is obtained. Finally,the proposed method isverified by the air pollutant data set of Hefei from 2017 to 2021. The results show that the model is better than other comparison models,which is feasible and effective.

相似文献/References:

[1]李东欣,禹 龙,田生伟,等.注意力机制的 LSTM-DBN 维语人称代词指代消解[J].计算机技术与发展,2019,29(07):33.[doi:10. 3969 / j. issn. 1673-629X. 2019. 07. 007]
　LI Dong-xin,YU Long,TIAN Sheng-wei,et al.Attention Mechanism of LSTM-DBN Uyghur Personal Pronoun Anaphora Resolution[J].,2019,29(03):33.[doi:10. 3969 / j. issn. 1673-629X. 2019. 07. 007]
[2]何 丽,袁 斌.利用长短期记忆网络进行音乐流派的分类[J].计算机技术与发展,2019,29(11):190.[doi:10. 3969 / j. issn. 1673-629X. 2019. 11. 038]
　HE Li,YUAN Bin.Classification of Music Genres Using Long and Short-term Memory Network[J].,2019,29(03):190.[doi:10. 3969 / j. issn. 1673-629X. 2019. 11. 038]
[3]王振业,叶成绪*,王文韬,等.基于 LSTM-Att 方法的音乐流行趋势预测[J].计算机技术与发展,2020,30(09):188.[doi:10. 3969 / j. issn. 1673-629X. 2020. 09. 034]
　WANG Zhen-ye,YE Cheng-xu*,WANG Wen-tao,et al.Music Trend Forecast Based on LSTM-Att Method[J].,2020,30(03):188.[doi:10. 3969 / j. issn. 1673-629X. 2020. 09. 034]
[4]吕 鹏,曹江涛,姬晓飞*.基于多任务学习的火灾检测算法[J].计算机技术与发展,2023,33(01):56.[doi:10. 3969 / j. issn. 1673-629X. 2023. 01. 009]
　LYU Peng,CAO Jiang-tao,JI Xiao-fei*.Fire Detection Algorithm Based on Multi-task Learning[J].,2023,33(03):56.[doi:10. 3969 / j. issn. 1673-629X. 2023. 01. 009]
[5]张钰洁,王 钰,杨杏丽.融合深度神经网络特征的 ARIMAX 雾霾 PM2. 5 预测[J].计算机技术与发展,2023,33(02):167.[doi:10. 3969 / j. issn. 1673-629X. 2023. 02. 025]
　ZHANG Yu-jie,WANG Yu,YANG Xing-li.Haze PM2. 5 Concentration Prediction Based on ARIMAX Model with Deep Convolutional Neural Network Features[J].,2023,33(03):167.[doi:10. 3969 / j. issn. 1673-629X. 2023. 02. 025]
[6]陈 雄,王海晨.基于 ISSA-LSTM 模型的短时交通流预测[J].计算机技术与发展,2023,33(04):198.[doi:10. 3969 / j. issn. 1673-629X. 2023. 04. 029]
　CHEN Xiong,WANG Hai-chen.Research on Traffic Flow Prediction Based on ISSA-LSTM Model[J].,2023,33(03):198.[doi:10. 3969 / j. issn. 1673-629X. 2023. 04. 029]

更新日期/Last Update: 2023-03-10