| 基于幅值立方和BP神经网络的表面肌电信号特征提取算法 |
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| 引用本文： | 黄鹏程,杨庆华,鲍官军,张立彬. 基于幅值立方和BP神经网络的表面肌电信号特征提取算法[J]. 中国机械工程, 2012, 23(11): 1332-1336 |
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| 作者姓名： | 黄鹏程 杨庆华 鲍官军 张立彬 |
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| 作者单位： | 浙江工业大学特种装备制造与先进加工技术教育部／浙江省重点实验室,杭州,310032 |
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| 基金项目： | 国家高技术研究发展计划(863计划)资助项目(2009AA04Z209);国家自然科学基金资助项目(51075363);浙江省自然科学基金杰出青年团队资助项目(R1090674) |
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| 摘 要： | 传统特征向量提取算法得到的特征向量无法识别手指不同角度动作,将幅值立方法引入特征向量的提取算法之中,并对立方计算后信号的特征量进行降数量级处理。实验表明,提出的特征向量提取算法对表面肌电信号微小特征差异的零偏差识别率达到75%,且大偏差保持在5%以下。设计的基于BP神经网络的手指运动模式分类器,能有效地提高手指运动形式的正确识别率。
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| 关 键 词： | 幅值立方法 神经网络 特征向量 偏差率 |
Feature Extraction Method Based on Amplitude Cubic and BP Neural Network of Surface ElectroMyoGraph(SEMG) Signals |
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| Huang Pengcheng Yang Qinghua Bao Guanjun Zhang Libin. Feature Extraction Method Based on Amplitude Cubic and BP Neural Network of Surface ElectroMyoGraph(SEMG) Signals[J]. China Mechanical Engineering, 2012, 23(11): 1332-1336 |
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| Authors: | Huang Pengcheng Yang Qinghua Bao Guanjun Zhang Libin |
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| Affiliation: | Huang Pengcheng Yang Qinghua Bao Guanjun Zhang Libin Key Laboratory of E&M,Ministry of Education & Zhejiang Province, Zhejiang University of Technology,Hangzhou,310032 |
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| Abstract: | Feature vector obtained from traditional characteristic vector extraction algorithm cannot identify the different angle motion of the finger,this paper introduced a method of amplitude cubic to the characteristic vector extraction algorithm,and then decreased the orders of magnitude.The comparison with traditional feature vector identification algorithms suggests that the zero error rate in identification to the tiny different SEMG feature of the feature vector extraction algorithms proposed reaches more than 75%,meanwhile the huge error rate remained below 5%.And the finger movement pattern recognizer based on BP neural network was designed to enhance the correct rate of the mode of finger movement. |
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| Keywords: | method of amplitude cubic neural network feature vector error rate |
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