中文核心期刊
CSCD来源期刊
中国科技核心期刊
RCCSE中国核心学术期刊

重庆交通大学学报(自然科学版) ›› 2007, Vol. 26 ›› Issue (6): 162-165.

• • 上一篇    

基于神经网络的混沌时序数据失真检测方法研究

王兴家1,汪晓惠2,赵超1   

  1. 1.重庆交通大学交通运输学院,重庆 400074;2.中国石油天燃气第七建设公司,山东 青岛 266300
  • 收稿日期:2006-09-13 修回日期:2006-11-08 出版日期:2007-12-15 发布日期:2015-01-22
  • 作者简介:王兴家(1977—),男,辽宁大连市人,硕士研究生,主要研究方向为车辆智能控制.E-mail:wxjldm@163.com

Study on Distortion Detective Method for Chaotic Time Series Based on Neural Network

WANG Xing-jia1,WANG Xiao-hui2,ZHA0 Chao1   

  1. 1.School of Transportation,Chongqing Jiaotong University,Chongqing 400074,China; 2.The 7th Construction Compary of China National Petroleum Corporation,Shandong Qingdao 266300,China
  • Received:2006-09-13 Revised:2006-11-08 Online:2007-12-15 Published:2015-01-22

摘要: 介绍了时序数据的混沌识别方法,并根据混沌时序数据的可预测性,提出了一种基于神经网络的混沌时序数 据失真检测方法.通过实例证明该方法能准确地检测出混沌时序数据的失真点,并将其还原.该方法既是检测过程 同时也是修复的过程,在数据处理领域有一定的应用前景.

关键词: 神经网络, 检测, 混沌

Abstract: The method of chaos identifying for time series is introduced.By virtue of the predictability of chaotic time series,the distortion detective method for chaotic time series based on neural network is presented.Th e practical example demonstrate that the distortion data of chaotic time series can be detected and recovered correctly by this kind of method.The method is not only a detective process but also a recovering process,and the application prospect in the field of data processing is optimistic.

Key words: neural network, detection, chaos

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