吉林大学学报(理学版)

• 计算机科学 • 上一篇    下一篇

基于残差修正的灰色神经网络在数据挖掘中的应用

孙金岭, 庞娟   

  1. 中国科学院 寒区旱区环境与工程研究所冰冻圈科学国家重点实验室, 兰州 730050;兰州理工大学 经济管理学院, 兰州 730050; 中国科学院大学, 北京 100049
  • 收稿日期:2015-01-13 出版日期:2015-11-26 发布日期:2015-11-23
  • 通讯作者: 孙金岭 E-mail:563521092@qq.com

Application of Gray Neural Network Based onResidual Correction in Data Mining

SUN Jinling, PANG Juan   

  1. State Key Laboratory of Cryospheric Sciences, Cold Arid Regions Environmental andEngineering Research Institute, Chinese Academy of Sciences, Lanzhou 730050, China; School of Economics and Management, Lanzhou University of Technology, Lanzhou 730050, China;The University of Chinese Acadamics of Sciences, Beijing 100049, China
  • Received:2015-01-13 Online:2015-11-26 Published:2015-11-23
  • Contact: SUN Jinling E-mail:563521092@qq.com

摘要:

针对时间序列数据样本少、 部分信息未知的特点, 提出将灰色理论与神经网络相结合构建灰色神经网络, 充分利用两种方法的优势对小样本时间序列数据进行有效挖掘. 为了提高模型的预测精度, 提出利用残差对模型进行有效修正. 实验分析表明, 残差修正灰色神经网络具有较高的预测精度, 适合于小样本时间序列数据的挖掘.

关键词: 数据挖掘, 灰色理论, 神经网络, 残差修正

Abstract:

In the light of the features of small sample size and some information unknown time series data, gray neural network was constructed by combining the gray theory with neural networks, which makes the full use of the advantage of the two kinds of approaches to realize the data mining of the small sample time series data effectively. Meanwhile, in order to improve the prediction accuracy of the model, the residuals were used for the model effective correction. The experiment results show that the proposed residual correction gray neural network has a high prediction accuracy, and is very suitable for the small sample time series data mining.

Key words: data mining, gray theory, neural networks, residual correction

中图分类号: 

  • TP311.13