CN107346459A - 一种基于遗传算法改进的多模式污染物集成预报方法 - Google Patents
一种基于遗传算法改进的多模式污染物集成预报方法 Download PDFInfo
- Publication number
- CN107346459A CN107346459A CN201710368411.XA CN201710368411A CN107346459A CN 107346459 A CN107346459 A CN 107346459A CN 201710368411 A CN201710368411 A CN 201710368411A CN 107346459 A CN107346459 A CN 107346459A
- Authority
- CN
- China
- Prior art keywords
- mrow
- msub
- population
- forecast
- msubsup
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 63
- 230000002068 genetic effect Effects 0.000 title claims abstract description 48
- 238000000034 method Methods 0.000 title claims abstract description 46
- 239000003344 environmental pollutant Substances 0.000 title claims abstract description 23
- 231100000719 pollutant Toxicity 0.000 title claims abstract description 23
- 230000003044 adaptive effect Effects 0.000 claims abstract description 13
- 238000012360 testing method Methods 0.000 claims abstract description 8
- 238000005457 optimization Methods 0.000 claims abstract description 4
- 238000002360 preparation method Methods 0.000 claims abstract description 4
- 238000012545 processing Methods 0.000 claims abstract description 4
- 238000012549 training Methods 0.000 claims description 20
- 230000007246 mechanism Effects 0.000 claims description 11
- 210000000349 chromosome Anatomy 0.000 claims description 10
- 210000002569 neuron Anatomy 0.000 claims description 8
- 230000001932 seasonal effect Effects 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 5
- 230000006870 function Effects 0.000 claims description 5
- 230000006872 improvement Effects 0.000 claims description 4
- 238000004088 simulation Methods 0.000 claims description 4
- 230000008859 change Effects 0.000 claims description 3
- 239000013256 coordination polymer Substances 0.000 claims description 3
- 238000004458 analytical method Methods 0.000 abstract description 6
- 238000013459 approach Methods 0.000 abstract description 6
- 238000013461 design Methods 0.000 abstract description 4
- 238000001556 precipitation Methods 0.000 abstract description 2
- 239000010410 layer Substances 0.000 description 14
- 230000009467 reduction Effects 0.000 description 8
- 238000010586 diagram Methods 0.000 description 7
- 230000008569 process Effects 0.000 description 5
- 238000013528 artificial neural network Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 238000002474 experimental method Methods 0.000 description 4
- 230000007935 neutral effect Effects 0.000 description 4
- 238000012216 screening Methods 0.000 description 4
- 230000004913 activation Effects 0.000 description 3
- 238000012417 linear regression Methods 0.000 description 3
- 238000013507 mapping Methods 0.000 description 3
- 108090000623 proteins and genes Proteins 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 230000000875 corresponding effect Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000007789 gas Substances 0.000 description 2
- 239000011229 interlayer Substances 0.000 description 2
- 238000012804 iterative process Methods 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 238000003062 neural network model Methods 0.000 description 2
- JCXJVPUVTGWSNB-UHFFFAOYSA-N nitrogen dioxide Inorganic materials O=[N]=O JCXJVPUVTGWSNB-UHFFFAOYSA-N 0.000 description 2
- 238000005096 rolling process Methods 0.000 description 2
- MGWGWNFMUOTEHG-UHFFFAOYSA-N 4-(3,5-dimethylphenyl)-1,3-thiazol-2-amine Chemical compound CC1=CC(C)=CC(C=2N=C(N)SC=2)=C1 MGWGWNFMUOTEHG-UHFFFAOYSA-N 0.000 description 1
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 description 1
- CBENFWSGALASAD-UHFFFAOYSA-N Ozone Chemical compound [O-][O+]=O CBENFWSGALASAD-UHFFFAOYSA-N 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 239000000443 aerosol Substances 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 229910002091 carbon monoxide Inorganic materials 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Biophysics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Biology (AREA)
- Software Systems (AREA)
- Mathematical Physics (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Molecular Biology (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- General Health & Medical Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Economics (AREA)
- Evolutionary Computation (AREA)
- Computational Linguistics (AREA)
- Biomedical Technology (AREA)
- Artificial Intelligence (AREA)
- Genetics & Genomics (AREA)
- Physiology (AREA)
- Development Economics (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
Description
Claims (5)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201710368411.XA CN107346459B (zh) | 2017-05-22 | 2017-05-22 | 一种基于遗传算法改进的多模式污染物集成预报方法 |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201710368411.XA CN107346459B (zh) | 2017-05-22 | 2017-05-22 | 一种基于遗传算法改进的多模式污染物集成预报方法 |
Publications (2)
Publication Number | Publication Date |
---|---|
CN107346459A true CN107346459A (zh) | 2017-11-14 |
CN107346459B CN107346459B (zh) | 2020-09-18 |
Family
ID=60253368
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201710368411.XA Active CN107346459B (zh) | 2017-05-22 | 2017-05-22 | 一种基于遗传算法改进的多模式污染物集成预报方法 |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN107346459B (zh) |
Cited By (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109492830A (zh) * | 2018-12-17 | 2019-03-19 | 杭州电子科技大学 | 一种基于时空深度学习的移动污染源排放浓度预测方法 |
CN109711592A (zh) * | 2018-03-27 | 2019-05-03 | 江苏信息职业技术学院 | 一种基于遗传算法优化极限学习机的池塘水温预测方法 |
CN109726867A (zh) * | 2018-12-27 | 2019-05-07 | 北京恒泰实达科技股份有限公司 | 一种基于多模式集合的高分辨率电力气象预报方法 |
CN110738641A (zh) * | 2019-10-07 | 2020-01-31 | 福州大学 | 基于图像处理及kelm的医药试剂浓度定性检测方法 |
CN111967600A (zh) * | 2020-08-18 | 2020-11-20 | 北京睿知图远科技有限公司 | 一种风控场景下基于遗传算法的特征衍生系统及方法 |
CN112965145A (zh) * | 2020-12-16 | 2021-06-15 | 陕西省环境监测中心站 | 一种环境空气臭氧预报方法 |
CN113011080A (zh) * | 2020-12-22 | 2021-06-22 | 浙江农林大学 | 一种负氧离子浓度反演方法 |
CN113420071A (zh) * | 2021-06-24 | 2021-09-21 | 天津大学 | 大气污染区域联防联控应急调控方案优选方法 |
Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
KR101133664B1 (ko) * | 2009-12-16 | 2012-04-12 | 한국건설기술연구원 | 분리막을 이용한 수처리 시스템에서 유전자 알고리즘/프로그래밍을 이용한 막오염지수 예측모델 기반 완화 세정 방법 및 시스템 |
CN103400190A (zh) * | 2013-08-13 | 2013-11-20 | 浙江大学 | 一种使用遗传算法优化极限学习机的集成框架方法 |
CN103888044A (zh) * | 2014-02-25 | 2014-06-25 | 江苏大学 | 一种模糊pid控制器的参数自整定方法 |
CN103955742A (zh) * | 2014-04-28 | 2014-07-30 | 淮阴工学院 | 一种基于集成学习的pm2.5预报方法 |
CN104680025A (zh) * | 2015-03-12 | 2015-06-03 | 重庆科技学院 | 基于遗传算法极限学习机的抽油机参数优化方法 |
CN105203869A (zh) * | 2015-09-06 | 2015-12-30 | 国网山东省电力公司烟台供电公司 | 一种基于极限学习机的微电网孤岛检测方法 |
CN106372731A (zh) * | 2016-11-14 | 2017-02-01 | 中南大学 | 一种强风高速铁路沿线风速空间网络构造预测方法 |
CN106570250A (zh) * | 2016-11-02 | 2017-04-19 | 华北电力大学(保定) | 面向电力大数据的微电网短期负荷预测方法 |
-
2017
- 2017-05-22 CN CN201710368411.XA patent/CN107346459B/zh active Active
Patent Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
KR101133664B1 (ko) * | 2009-12-16 | 2012-04-12 | 한국건설기술연구원 | 분리막을 이용한 수처리 시스템에서 유전자 알고리즘/프로그래밍을 이용한 막오염지수 예측모델 기반 완화 세정 방법 및 시스템 |
CN103400190A (zh) * | 2013-08-13 | 2013-11-20 | 浙江大学 | 一种使用遗传算法优化极限学习机的集成框架方法 |
CN103888044A (zh) * | 2014-02-25 | 2014-06-25 | 江苏大学 | 一种模糊pid控制器的参数自整定方法 |
CN103955742A (zh) * | 2014-04-28 | 2014-07-30 | 淮阴工学院 | 一种基于集成学习的pm2.5预报方法 |
CN104680025A (zh) * | 2015-03-12 | 2015-06-03 | 重庆科技学院 | 基于遗传算法极限学习机的抽油机参数优化方法 |
CN105203869A (zh) * | 2015-09-06 | 2015-12-30 | 国网山东省电力公司烟台供电公司 | 一种基于极限学习机的微电网孤岛检测方法 |
CN106570250A (zh) * | 2016-11-02 | 2017-04-19 | 华北电力大学(保定) | 面向电力大数据的微电网短期负荷预测方法 |
CN106372731A (zh) * | 2016-11-14 | 2017-02-01 | 中南大学 | 一种强风高速铁路沿线风速空间网络构造预测方法 |
Non-Patent Citations (1)
Title |
---|
陈焕盛 等: "空气质量多模式系统在广州应用及对PM10预报效果评估", 《气候与环境研究》 * |
Cited By (13)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109711592A (zh) * | 2018-03-27 | 2019-05-03 | 江苏信息职业技术学院 | 一种基于遗传算法优化极限学习机的池塘水温预测方法 |
CN109492830B (zh) * | 2018-12-17 | 2021-08-31 | 杭州电子科技大学 | 一种基于时空深度学习的移动污染源排放浓度预测方法 |
CN109492830A (zh) * | 2018-12-17 | 2019-03-19 | 杭州电子科技大学 | 一种基于时空深度学习的移动污染源排放浓度预测方法 |
CN109726867A (zh) * | 2018-12-27 | 2019-05-07 | 北京恒泰实达科技股份有限公司 | 一种基于多模式集合的高分辨率电力气象预报方法 |
CN109726867B (zh) * | 2018-12-27 | 2020-07-28 | 北京恒泰实达科技股份有限公司 | 一种基于多模式集合的高分辨率电力气象预报方法 |
CN110738641A (zh) * | 2019-10-07 | 2020-01-31 | 福州大学 | 基于图像处理及kelm的医药试剂浓度定性检测方法 |
CN110738641B (zh) * | 2019-10-07 | 2022-07-01 | 福州大学 | 基于图像处理及kelm的医药试剂浓度定性检测方法 |
CN111967600A (zh) * | 2020-08-18 | 2020-11-20 | 北京睿知图远科技有限公司 | 一种风控场景下基于遗传算法的特征衍生系统及方法 |
CN112965145A (zh) * | 2020-12-16 | 2021-06-15 | 陕西省环境监测中心站 | 一种环境空气臭氧预报方法 |
CN112965145B (zh) * | 2020-12-16 | 2021-09-21 | 陕西省环境监测中心站 | 一种环境空气臭氧预报方法 |
CN113011080A (zh) * | 2020-12-22 | 2021-06-22 | 浙江农林大学 | 一种负氧离子浓度反演方法 |
CN113011080B (zh) * | 2020-12-22 | 2024-04-19 | 浙江农林大学 | 一种负氧离子浓度反演方法 |
CN113420071A (zh) * | 2021-06-24 | 2021-09-21 | 天津大学 | 大气污染区域联防联控应急调控方案优选方法 |
Also Published As
Publication number | Publication date |
---|---|
CN107346459B (zh) | 2020-09-18 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN107346459A (zh) | 一种基于遗传算法改进的多模式污染物集成预报方法 | |
Kim et al. | Neural network model incorporating a genetic algorithm in estimating construction costs | |
CN112906982A (zh) | 一种基于gnn-lstm结合的网络流量预测方法 | |
CN106529818B (zh) | 基于模糊小波神经网络的水质评价预测方法 | |
CN106570597A (zh) | 一种sdn架构下基于深度学习的内容流行度预测方法 | |
CN108009674A (zh) | 基于cnn和lstm融合神经网络的空气pm2.5浓度预测方法 | |
CN110674999A (zh) | 基于改进聚类和长短期记忆深度学习的小区负荷预测方法 | |
CN107506590A (zh) | 一种基于改进深度信念网络的心血管疾病预测模型 | |
CN103105246A (zh) | 一种基于遗传算法改进的bp神经网络的温室环境预测反馈方法 | |
CN106650920A (zh) | 一种基于优化极限学习机的预测模型 | |
CN112989635B (zh) | 基于自编码器多样性生成机制的集成学习软测量建模方法 | |
CN109242223A (zh) | 城市公共建筑火灾风险的量子支持向量机评估与预测方法 | |
Shi et al. | How can China achieve the 2030 carbon peak goal—a crossover analysis based on low-carbon economics and deep learning | |
Amirteimoori et al. | On the environmental performance analysis: a combined fuzzy data envelopment analysis and artificial intelligence algorithms | |
CN104503420A (zh) | 一种基于新型fde-elm和时延efsm的非线性过程工业故障预测方法 | |
CN112634019A (zh) | 基于细菌觅食算法优化灰色神经网络的违约概率预测方法 | |
CN105844334B (zh) | 一种基于径向基神经网络的温度插值方法 | |
CN115186803A (zh) | 一种考虑pue的数据中心算力负荷需求组合预测方法和系统 | |
CN108647772A (zh) | 一种用于边坡监测数据粗差剔除的方法 | |
CN114742209A (zh) | 一种短时交通流预测方法及系统 | |
CN108537581B (zh) | 基于gmdh选择性组合的能源消费量时间序列预测方法及装置 | |
CN109408896A (zh) | 一种污水厌氧处理产气量多元智能实时监控方法 | |
Lizhe et al. | BP network model optimized by adaptive genetic algorithms and the application on quality evaluation for class teaching | |
Jian-Ming | Traffic prediction based on improved neural network | |
Mu et al. | An improved effective approach for urban air quality forecast |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant | ||
TR01 | Transfer of patent right | ||
TR01 | Transfer of patent right |
Effective date of registration: 20210716 Address after: ACDF, 6th floor, block a, building 7, Baoneng Science Park, Qinghu Industrial Park, Qingxiang Road, Longhua office, Longhua New District, Shenzhen, Guangdong 518000 Patentee after: SHENZHEN ANRUAN TECHNOLOGY Co.,Ltd. Address before: 300222 Tianjin University of Science and Technology, 1038 South Road, Tianjin, Hexi District, Dagu Patentee before: Tianjin University of Science and Technology |
|
PP01 | Preservation of patent right | ||
PP01 | Preservation of patent right |
Effective date of registration: 20240109 Granted publication date: 20200918 |