传感技术学报2017，Vol.30Issue(10)：1459-1464,6.DOI:10.3969/j.issn.1004-1699.2017.10.001
改进的P SO-SVM在表面肌电信号模式识别中的研究
A Support Vector Machine Based on an Improved Particle Swarm Optimization Algorithm for SEMG Signal Pattern Recognition
摘要
Abstract
In order to improve the motion pattern recognition rate of EMG signals,this paper proposes an improved PSO algorithm to optimize SVM( IPSO-SVM) . Firstly,IPSO-SVM introduces a way to simplify the position and ve-locity formulas of PSO,then proposes ESE state estimation for premature convergence,and finally adopts 5 test algo-rithms to classify the six hand motion patterns recognition( fist clenching,fist unfolding,internal and external rota-tion,wrist intorsion and wrist extorsion). The results showed that the average accuracy rate of IPSO-SVM is 93.75%and the average accuracy of traditional SVM algorithm is 70.21%;the training and testing time were also obviously reduced. It also has strong robustness and noise immunity. Therefore,the IPSO-SVM algorithm can be used to solve the classification problem of the surface EMG signal,which has a good application value.关键词
表面肌电信号/模式识别/粒子群优化算法/支持向量机Key words
surface electromyography signal/pattern recognition/particle swarm optimization algorithm/support vector machine分类
信息技术与安全科学引用本文复制引用
顾明亮,刘俊..改进的P SO-SVM在表面肌电信号模式识别中的研究[J].传感技术学报,2017,30(10):1459-1464,6.基金项目
上海电机学院登峰学科机械工程支持项目(16DFXK01) （16DFXK01）
