Research of forecasting of air quality based on optimization of Evolutionary Neural Network

doi:10.3778/j.issn.1002-8331.2009.28.064

Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (28): 211-214.DOI: 10.3778/j.issn.1002-8331.2009.28.064

• 工程与应用 • Previous Articles Next Articles

Research of forecasting of air quality based on optimization of Evolutionary Neural Network

ZHANG Qi¹，LUO Guo-liang¹，LI Jia¹，ZHAO Kun-rong²

1.College of Computer Science and Engineering，South China University of Technology，Guangzhou 510006，China
2.South China Institute of Environment Science，National Bureau of Environmental Protection，Guangzhou 510655，China

Received:2009-03-30 Revised:2009-05-20 Online:2009-10-01 Published:2009-10-01
Contact: ZHANG Qi

优化进化神经网络的空气质量预测研究

张齐¹，罗国亮¹，李佳¹，赵坤荣²

1.华南理工大学计算机科学与工程学院，广州 510006
2.环境保护部华南环境科学研究所，广州 510655

通讯作者: 张齐

Abstract

Abstract: Evolutionary Neural Network（ENN） does not concern problems’ internal mechanism，avoids the convergence to local brought by neural network model，but the ENN model is complicated because of too many parameters.This paper optimizes the Simple ENN（SENN） with individual encoding，fitness function，and develops self-adaptive crossing rate and mutation rate of the species，which are main factors that influence the performance of the ENN.Simulation of the concentration that SO2 contained in the air with its impact factors and the historical data shows that Optimized ENN（OENN） converges faster in the evolution process than the SENN，and performs better in the forecasting.The OENN model can be capable to supply valuable prediction data for environmental protection.

Key words: Evolutionary Neural Network（ENN）, pollution sources, prediction

摘要： 进化神经网络模型与问题内部机制无关，避免了神经网络收敛到局部，但模型存在参数多而过于复杂的问题。对影响基本进化神经网络模型性能的个体编码方式和适应度函数进行优化，并自适应性定义种群交叉率、变异率。以大气中主要污染物SO2为例，考虑气温、相对湿度、风速等影响因子，实验仿真结果表明优化后的进化神经网络较传统的基本进化神经网络模型进化过程收敛更快，预测效果更佳，为环境保护部门提供可靠的决策依据。

关键词: 进化神经网络, 污染源, 预测

CLC Number:

TP183

ZHANG Qi¹，LUO Guo-liang¹，LI Jia¹，ZHAO Kun-rong². Research of forecasting of air quality based on optimization of Evolutionary Neural Network[J]. Computer Engineering and Applications, 2009, 45(28): 211-214.

张齐¹，罗国亮¹，李佳¹，赵坤荣². 优化进化神经网络的空气质量预测研究[J]. 计算机工程与应用, 2009, 45(28): 211-214.

[1]	HUANG Dongyi, YANG Bing, WU Zihao, KUANG Jiayi, YAN Zeming. Spatio-Temporal Fully Connected Convolutional Neural Networks for Citywide Cellular Prediction [J]. Computer Engineering and Applications, 2021, 57(9): 168-175.
[2]	YANG Li, WU Yi, WEI Debin, PAN Chengsheng. Satellite Network Traffic Prediction Based on Spatiotemporal Correlation [J]. Computer Engineering and Applications, 2021, 57(7): 101-106.
[3]	CHANG Hao, CHEN Xiaolei, ZHANG Aihua, LI Ce, LIN Dongmei. Continuous Blood Pressure Prediction Based on Improved SENet Convolutional Neural Network [J]. Computer Engineering and Applications, 2021, 57(7): 130-135.
[4]	LIU Ziyan, YUAN Lei, ZHU Mingcheng, MA Shanshan, CHEN Linzhouting. YOLOv3 Traffic sign Detection based on SPP and Improved FPN [J]. Computer Engineering and Applications, 2021, 57(7): 164-170.
[5]	ZHANG Rui, WU Boxiong, ZHANG Liyuan, ZHANG Bo. Human Trajectory Prediction Method for Complex Scenes [J]. Computer Engineering and Applications, 2021, 57(6): 138-143.
[6]	YANG Fengyu, HUANG Yaxuan, ZHOU Shijian, ZHENG Wei. Survey of Software Defect Prediction Combined with Multi-metrics [J]. Computer Engineering and Applications, 2021, 57(5): 10-24.
[7]	LI Shuo, LIANG Yi. Prediction Model of Execution Time for Batch Application in Spark [J]. Computer Engineering and Applications, 2021, 57(5): 79-87.
[8]	XU Xianfeng, CAI Lulu, ZHANG Li. Photovoltaic Power Generation Prediction Algorithm Based on MLP and DBN [J]. Computer Engineering and Applications, 2021, 57(3): 266-272.
[9]	ZHENG Jianfeng, WANG Yingming. Research on Efficiency Confidence Interval Prediction Model Based on DEA-BP Neural Network [J]. Computer Engineering and Applications, 2021, 57(3): 273-278.
[10]	AN Weichao, YAN Ting, ZHANG Nan, ZHANG Shan, XIANG Jie, CAO Rui, WANG Bin. Application of Pathological Image Texture Analysis in MSI Prediction of Gastric Cancer [J]. Computer Engineering and Applications, 2021, 57(24): 205-211.
[11]	CHEN Hai, QIAN Fulan, CHEN Jie, ZHAO Shu, ZHANG Yanping. Rating Prediction Model Based on Variational Auto-Encoder [J]. Computer Engineering and Applications, 2021, 57(22): 153-159.
[12]	YI Lingzhi, WANG Shitong, YI Fang, DENG Dong, YI Zhimin, JIANG Peng. Wind Farm Ultra-Short-Term Wind Speed Prediction Based on EEMDSE-ILSTM [J]. Computer Engineering and Applications, 2021, 57(22): 288-294.
[13]	WU Minghui, HOU Lingyan, WANG Chao. Improved Mechanism of Prediction-Oriented Long Short-Term Memory Neural Network [J]. Computer Engineering and Applications, 2021, 57(21): 109-115.
[14]	ZHAO Yu, ZHU Yi, YU Qiao, CHEN Xiaoying. Cross Project Defect Prediction Method Based on Hierarchical Data Screening [J]. Computer Engineering and Applications, 2021, 57(20): 279-286.
[15]	ZHANG Xueting, CHENG Hua, FANG Yiquan. Prediction of Co-authorship Based on Meta-Path and Node Attributes [J]. Computer Engineering and Applications, 2021, 57(2): 164-169.

Research of forecasting of air quality based on optimization of Evolutionary Neural Network

优化进化神经网络的空气质量预测研究

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics