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.
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.
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2011年第4期485-488,共4页
Journal of Northeastern University(Natural Science)
基金
国家自然科学基金资助项目(50477015)
中央高校基本科研业务费专项资金资助项目(N100604006)
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
chaotic phase state classification
improved pulse coupled neural network
weak signal detection
feature extraction
ECG signal
