ARTIFICIAL NEURAL NETWORK CORRECTION OF ROUGH ERRORS OF OBSERVATIONS*

+ Author Affiliations + Find other works by these authors
  • Funds:

    * This work is supported jointly by the Project of Funds for University-Level Remarkable Teachers,Education Ministry of China; by the Program of 2000 South-China Sea Flux Observations.

PDF

  • In the context of tower measured radiation datasets.following the correction principle meeting a diagnostic equation in data quality control and in terms of a technique for model construction on data and ANN (artificial neural network) retrieval for BP correction of radiation measurements with rough errors available,a BP model is presented.Evidence suggests that the developed model works well and is superior to a convenient multivariate linear regression model,indicating its wide applications.
  • 加载中
  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

ARTIFICIAL NEURAL NETWORK CORRECTION OF ROUGH ERRORS OF OBSERVATIONS*

  • 1. Institute of Meteorology,PLA University of Sciences and Engineering,Nanjing 211101;
    Institute of Meteorology,PLA University of Sciences and Engineering,Nanjing 211101;
    Institute of Meteorology,PLA University of Sciences and Engineering,Nanjing 211101;
    YAO Huadong National Climate Center,Beijing 100081;
    YAO Huadong National Climate Center,Beijing 100081
Funds: * This work is supported jointly by the Project of Funds for University-Level Remarkable Teachers,Education Ministry of China; by the Program of 2000 South-China Sea Flux Observations.

Abstract: In the context of tower measured radiation datasets.following the correction principle meeting a diagnostic equation in data quality control and in terms of a technique for model construction on data and ANN (artificial neural network) retrieval for BP correction of radiation measurements with rough errors available,a BP model is presented.Evidence suggests that the developed model works well and is superior to a convenient multivariate linear regression model,indicating its wide applications.

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return