基于改进回声状态网络的人体脉象识别[EB/OL]
北京：中国科技论文在线
通过改进的回声状态网络训练和识别12种人体脉象
is fully considered
Based on traditional Chinese medicine（TCM） pulse theory, the pulse time domain and frequency domain feature parameters are extracted, and then the correlation dimension, maximum Lyapunov exponent and Kolmogorov entropy, which are used as the chaos feature parameters of pulse and used to quantitatively verify that the pulse is a typical chaotic signal, are received by calculating in the reconstructed multidimensional phase space of pulse
Finally, the improved echo state network (ESN) identifier, whose activation function is switched to non-symmetric function combined with chaos theory, is designed and used to train and test 12 kinds of pulse patterns
And the main parameters of the novel neural network are optimized by particle swarm optimization (PSO) algorithm
Experiments show that the pulse feature parameters are selected effectively and the improved ESN neural network is more superior to feedforward neural networks, such as back propagation (BP) neural network, probabilistic neural network (PNN) and radial basis function (RBF) neural network
Keywords： Pulse patterns recognition; feature parameters extraction; chaotic features analysis; echo state network; PSO algorithm 下载PDF阅读器 PDF全文下载： 初稿 （ 310 ） 作者简介： Yang Ling(1966- ),female,associate professor, master instructor
The research area includes pattern recognition, image processing, and information fusion
通信联系人： Wang Ruxu(1979- ),male, master,the research areas: pattern recognition, bio-signal processing
【收录情况】 中国科技论文在线： 杨凌
然后又提取了用于证明人体脉象是混沌信号的3个定量值
脉象特征值的选取比较有效
关键词： 人体脉象识别
Zhang Wenbo 2, （ 1、 College of information science and engineering,Lanzhou University
