某混沌实时判定系统中特征提取的原因分析  

Reason of feature extraction in the system for real-time identification of chaos

作　　者：卢宇[1] 阎利[2] 张龙斌[3] 

机构地区：[1]清华大学航天航空学院,北京100084 [2]安阳工学院建筑工程系,河南安阳455000 [3]天津大学系统工程研究所,天津300072

出　　处：《河北工程大学学报（自然科学版）》2009年第3期67-72,共6页Journal of Hebei University of Engineering:Natural Science Edition

基　　金：国家自然科学基金资助项目(50478088)

摘　　要：研究混沌判定方法,对进行混沌分析与控制具有重要意义。基于信息论、控制论、数据挖掘的一些基本原理和方法,描述了某混沌实时判定系统的结构与功能模块。为满足该判定系统原理及功能的要求,需要进行时间序列的特征提取。以Logistic系统为例进行了实验研究,应用小波包分析、小波包能量进行了特征提取,分析了进行特征提取的原因与必要性。实验结果表明,特征提取模块是整个判定系统的关键模块,小波包能量可以作为该系统中特征提取的方法。The identification of chaos phenomenon is important for chaos analysis and control. Based on the conceptions and methods of information theory, control theory and data mining techniques, the framework and function module of the system for real - time identification of chaos are introduced. To meet the requirement of the principle and function of this system, feature extraction for time series is necessary. The case studies are given for time series generated by Logistic system. Feature of time series are extracted by wavelet packet coefficients and wavelet packet energy respectively. The reason and necessity of feature extraction are analyzed. The results indicate that the module for feature extraction is a key module in the system for real - time identification of chaos. Wavelet packet energy can be used in this system for feature extraction.

关 键 词：混沌 实时判定 时间序列 特征提取 小波分析 小波包能量 

分 类 号：U491[交通运输工程—交通运输规划与管理]