东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2011, Vol. 32 ›› Issue (4): 485-488.DOI: -

• 论著 • 上一篇    下一篇

基于改进型脉冲耦合神经网络的混沌相态分类方法

蒋芳芳;王旭;杨丹;   

  1. 东北大学信息科学与工程学院;
  • 收稿日期:2013-06-19 修回日期:2013-06-19 发布日期:2013-04-04
  • 通讯作者: -
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(50477015);;

Chaotic phase state classification based on an improved pulse coupled neural network

Jiang, Fang-Fang (1); Wang, Xu (1); Yang, Dan (1)   

  1. (1) School of Information Science and Engineering, Northeastern University, Shenyang 110819, China
  • Received:2013-06-19 Revised:2013-06-19 Published:2013-04-04
  • Contact: Jiang, F.-F.
  • About author:-
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摘要： 混沌相态分类是利用混沌系统检测微弱信号的关键步骤.提出一种基于改进型脉冲耦合神经网络的混沌相态分类方法.利用该网络模拟哺乳动物视觉皮层神经细胞活动的特点,提取混沌系统输出相态图的结构特征,并应用均值残差算法进行特征信息降维,进而实现对系统混沌态与周期态的实时判别.以Lyapunov特性指数方法作为评价准则,分别使用正弦信号和标准ECG信号对所提方法进行检验,实验结果表明,所提方法可以快速、准确地对不同的混沌相态进行分类.

关键词: 混沌相态分类, 改进型脉冲耦合神经网络, 微弱信号检测, 特征提取, 心电信号

Abstract: Chaotic phase state classification is a key step in utilizing chaotic systems to detect weak signals. A chaotic phase state classification method based on an improved pulse coupled neural network (PCNN) was proposed. The movement characteristics of mammalian visual cortical neurons were simulated with improved PCNN, while outputting the structural characteristics of a phase state diagram of a chaotic system. Dimensions of these characteristics were reduced using average residual error to yield real-time classification for chaotic/periodic states of the system. Sinusoidal and ECG signals were used to verify the proposed method using Lyapunov characteristic exponents. Results indicate that the method can quickly and accurately classify different chaotic phase states.

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