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基于FFT盲辨识的肌电信号建模及模式识别

李阳 田彦涛 陈万忠

李阳, 田彦涛, 陈万忠. 基于FFT盲辨识的肌电信号建模及模式识别. 自动化学报, 2012, 38(1): 128-134. doi: 10.3724/SP.J.1004.2012.00128
引用本文: 李阳, 田彦涛, 陈万忠. 基于FFT盲辨识的肌电信号建模及模式识别. 自动化学报, 2012, 38(1): 128-134. doi: 10.3724/SP.J.1004.2012.00128
LI Yang, TIAN Yan-Tao, CHEN Wan-Zhong. Modeling and Classifying of sEMG Based on FFT Blind Identification. ACTA AUTOMATICA SINICA, 2012, 38(1): 128-134. doi: 10.3724/SP.J.1004.2012.00128
Citation: LI Yang, TIAN Yan-Tao, CHEN Wan-Zhong. Modeling and Classifying of sEMG Based on FFT Blind Identification. ACTA AUTOMATICA SINICA, 2012, 38(1): 128-134. doi: 10.3724/SP.J.1004.2012.00128

基于FFT盲辨识的肌电信号建模及模式识别

doi: 10.3724/SP.J.1004.2012.00128
详细信息
    通讯作者:

    田彦涛吉林大学教授.1993年于吉林工业大学获得工学博士学位.主要研究方向为复杂系统建模,优化与控制,机器视觉与模式识别.本文通信作者.E-mail: tianyt@jlu.edu.cn

Modeling and Classifying of sEMG Based on FFT Blind Identification

  • 摘要: 针对表面肌电信号(Electromyographic signal,sEMG)产生原理复杂、易受人体自身及外界因素影响的特点,采用基于快速傅里叶变换(Fast Fourier transform,FFT)的盲辨识方法建立肌电信号模型.该方法通过计算即可确定信道阶次,无需人为凭借经验设定,且计算简单、易于实现、运算速度快.其利用输出信道间的相互关系特性,实现信号的频域盲辨识,建立数学模型.此方法适用于小样本信号建模,非常适合易受肌肉疲劳影响的表面肌电信号.将模型系数作为改进的BP神经网络的输入,实现多运动模式识别,与其他盲辨识方法比较,此方法识别效果较好.
  • [1] Li Y,Tian Y T,Chen W Z. Multi-pattern recognition of sEMG based on improved BP neural network algorithm. In:Proceedings of the 29th Chinese Control Conference. Beijing,China:IEEE,2010. 2867-2872[2] Xu G,Liu H,Tong L,Kailath T. A least-squares approach to blind channel identification. IEEE Transactions on Signal Processing,1995,43(12):2982-2993[3] Bai E W,Fu M Y. A blind approach to Hammerstein model identification. IEEE Transactions on Signal Processing,2002,50(7):1610-1619[4] Narasimhan S V,Hazarathaiah M,Giridhar P. Channel blind identification based on cyclostationarity and group delay. Signal Processing,2005,85(7):1275-1286[5] Fang J,Leymanb A R,Chew Y H,Duan H P. Some further results on blind identification of MIMO FIR channels via second-order statistics. Signal Processing,2007,87(6):1434-1447[6] Xu Xiao-Ping,Qian Fu-Cai,Wang Feng. New method for identification of Wiener-Hammerstein model. Control and Decision,2008,23(8):929-934(徐小平,钱富才,王峰. 一种辨识Wiener-Hammerstein模型的新方法. 控制与决策,2008,23(8):929-934)[7] Li Y,Tian Y T,Shang X J,Chen W Z. Modeling and classification of sEMG based on blind identification theory. In:Proceedings of the 8th International Symposium on Neural Networks. Guilin,China:Springer,2011. 340-347[8] Shang X J,Tian Y T,Li Y. Modeling and classification of sEMG based on instrumental variable identification. In:Proceedings of the 8th International Symposium on Neural Networks. Guilin,China:IEEE,2011. 331-339[9] Huang Y,Jacob B,Chen J. Using the Pearson correlation coefficient to develop an optimally weighted cross relation based SIMO identification algorithm. In:Proceedings of the IEEE International Conference on Acoustics,Speech and Signal Processing. Taipei,China:IEEE,2009. 3153-3156[10] Chen Rong,Xu Yong-Mao,Lan Hong-Sen. Research on multilayered feedforward neural networks:genetic back propagation algorithm and structure optimization strategy. Acta Automatica Sinica,1997,23(1):43-49(陈荣,徐用懋,兰鸿森. 多层前向网络的研究---遗传BP算法和结构优化策略. 自动化学报,1997,23(1):43-49)[11] Yang Juan,Lu Yang,Huang Zhen-Jin,Wang Qiang. Hamming sphere dimple in binary neural networks and its linear separability. Acta Automatica Sinica,2011,37(6):737-745(杨娟,陆阳,黄镇谨,王强. 二进神经网络中的汉明球突及其线性可分性. 自动化学报,2011,37(6):737-745)[12] Wang Li-Fang,Zeng Jian-Chao. A cooperative evolutionary algorithm based on particle swarm optimization and simulated annealing algorithm. Acta Automatica Sinica,2006,32(4):630-635(王丽芳,曾建潮. 基于微粒群算法与模拟退火算法的协同进化方法. 自动化学报,2006,32(4):630-635)[13] Ban Xiao-Juan,Liu Hao,Xu Zhuo-Ran. An energy artificial neuron model based self-growing and self-organizing neural network. Acta Automatica Sinica,2011,37(5):615-622(班晓娟,刘浩,徐卓然. 一种基于能量人工神经元模型的自生长、自组织神经网络. 自动化学报,2011,37(5):615-622)
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出版历程
  • 收稿日期:  2011-01-13
  • 修回日期:  2011-07-16
  • 刊出日期:  2012-01-20

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