东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2010, Vol. 31 ›› Issue (12): 1693-1696.DOI: -

• 论著 • 上一篇    下一篇

基于改进RBF网络的过程工业时间序列预测方法

刘芳;毛志忠;李磊;   

  1. 东北大学信息科学与工程学院;东北大学流程工业综合自动化教育部重点实验室;
  • 收稿日期:2013-06-20 修回日期:2013-06-20 出版日期:2010-12-15 发布日期:2013-06-20
  • 通讯作者: -
  • 作者简介:-
  • 基金资助:
    国家高新技术研究发展计划项目(2007AA04Z194;2007AA041401)

RBFN-based prediction for time series of process industries

Liu, Fang (1); Mao, Zhi-Zhong (1); Li, Lei (1)   

  1. (1) School of Information Science and Engineering, Northeastern University, Shenyang 110004, China; (2) Key Laboratory of Integrated Automation of Process Industry, Ministry of Education, Northeastern University, Shenyang 110004, China
  • Received:2013-06-20 Revised:2013-06-20 Online:2010-12-15 Published:2013-06-20
  • Contact: Liu, F.
  • About author:-
  • Supported by:
    -

RichHTML

0

PDF (PC)

507

摘要： 基于残差思想的异常数据检测方法的关键之处在于对数据的准确预测.针对这一问题,提出基于改进径向基网络(radial basis function network,RBFN)的过程工业时间序列预测方法,该方法通过改变RBF网络的输入形式,使改进后的RBF网络能够更方便地引入遗忘因子以及惩罚因子,以适应于基于残差思想的异常数据检测方法要求的动态性能和鲁棒性.通过理论证明改进的RBF网络与传统RBF网络的等效性,并通过实验比较证明改进后的RBF网络较传统的网络结构更简单,参数意义更明确.

关键词: 径向基网络, 异常数据检测, 时间序列, 过程工业, 高斯函数

Abstract: The key of residual-based outlier detection algorithm depends on the accurate prediction. To solve the problem, a prediction algorithm based on the improved RBFN (radial basis function network) is proposed for time series of process industries. In the algorithm the input form of the improved RBFN is changed so as to enable the improved RBFN to be more convenient for introducing the forgetting and penalty factors into it, thus adapting itself to the dynamic performance/robustness required by the residual-based outlier detection. The equivalence between the improved RBFN and conventional RBFN has been proved theoretically, and the comparative test results revealed that the former is simpler than the latter in network architecture with more definite attributes of parameter concerned.

中图分类号: