| 用LM算法改进的BP网络在表面肌电信号识别中的应用研究 |
| |
| 引用本文： | 张坤,王志中. 用LM算法改进的BP网络在表面肌电信号识别中的应用研究[J]. 中国医疗器械杂志, 2005, 29(6): 399-401 |
| |
| 作者姓名： | 张坤 王志中 |
| |
| 作者单位： | 上海交通大学生物医学工程系,上海,200030 |
| |
| 摘 要： | 提出用Levenberg-Marquardt算法改进BP神经网络识别表面肌电信号的方法.采用多尺度小波变换对肌电信号进行分析,提取各尺度下小波系数幅值的最大和最小值构造特征矢量,输入BP神经网络可进行模式识别,经过训练能够成功地从表面肌电信号中识别展拳、握拳、前臂内旋、前臂外旋四种运动模式.实验表明,LM算法在响应时间和识别精度上都比标准的BP算法有了很大提高.
|
| 关 键 词： | 小波变换 BP神经网络 LM算法 肌电信号 |
| 文章编号： | 1671-7104(2005)06-0399-03 |
| 修稿时间： | 2005-05-30 |
The Application of BP Neural Network Improved with LM Algorithm in Surface EMG Signal Classification |
| |
| ZHANG Kun,WANG Zhi-zhong. The Application of BP Neural Network Improved with LM Algorithm in Surface EMG Signal Classification[J]. Chinese journal of medical instrumentation, 2005, 29(6): 399-401 |
| |
| Authors: | ZHANG Kun WANG Zhi-zhong |
| |
| Affiliation: | Department of Biomedical Engineering, Shanghai JiaoTong University, Shanghai. |
| |
| Abstract: | The method of BP neural network improved by Levenberg-Marquardt algorithm in surface EMG signal classification is proposed.The data reduction and preprocessing operations of the signals are performed by means of the wavelet transform.The classifier can identify four classes of forearm movement:hand extension,hand grasp,forearm pronation and forearm supination with a high accuracy.Experimental result shows that the BP neural netwok improved by LM algorithm has greatly increased the speed and the accuracy of signal classification in practical application of prothesis control. |
| |
| Keywords: | wavelet transformation BP neural network LM algorithm surface EMG signal |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
