摘要
为了提高表面肌电信号(surface Electromyographic signal, sEMG)手势动作识别的准确率,本文提出基于双权重粒子群算法(Particle Swarm Optimization, PSO)优化支持向量机(Support Vector Machine, SVM)的分类模型(DWPSO-SVM)。针对传统PSO在参数寻优时易陷入“早熟”问题,进一步提高粒子寻优能力,本文在标准PSO的基础上引入约束因子结合同向更新策略用于速度约束,有效的提高了粒子的寻优能力并缓解了“早熟”现象;其次,分析了多种权重更新策略对惯性权重和约束因子的影响;最终,采用非线性更新策略结合DWPSO优化SVM模型构建特征分类模型。实验表明,本文提出的DWPSO-SVM模型能够有效完成sEMG手势动作识别任务。
In order to improve the accuracy of surface electromyographic signal(sEMG)gesture recognition,this paper proposes a classification model(DWPSO-SVM)based on Particle Swarm Optimization(PSO)to optimize Support Vector Machine(SVM).In response to the problem of premature convergence in parameter optimization of traditional PSO,and further improving the particle optimization ability,this paper introduces a constraint factor and a directed following strategy based on the standard PSO for speed constraints,effectively improving the particle optimization ability and alleviating the phenomenon of premature convergence;Secondly,the impact of various weight update strategies on inertia weights and constraint factors was analyzed;Finally,a non-linear update strategy combined with DWPSO optimization SVM was used to construct a feature classification model.The experiment shows that the DWPSO-SVM model proposed in this article can effectively complete the sEMG gesture action recognition task.
作者
王宇春
王敏
袁东华
赵化启
WANG Yuchun;WANG Min;YUAN Donghua;ZHAO Huaqi(College of Information and Electronic Technology,Jiamusi University,Jiamusi Heilongjiang 154007,China)
出处
《智能计算机与应用》
2023年第12期158-164,共7页
Intelligent Computer and Applications
基金
黑龙江省省属本科高校基本科研业务费科研项目(2018-KYYWF-0943)
黑龙江省卫生健康委立项科研课题(2019-287)
佳木斯大学优秀学科团队项目(JDXKTD-2019008)
佳木斯大学教育教学改革研究项目(2021JY1-49)。
关键词
SEMG
粒子群算法
支持向量机
手势动作识别
sEMG
Particle Swarm Optimization
Support Vector Machine
gesture action recognition
作者简介
王宇春(1979-),男,博士,讲师,主要研究方向:生物医学仪器及信号分析;王敏(1999-),女,硕士研究生,主要研究方向:生物医学仪器及肌电信号识别;袁东华(1999-),男,硕士研究生,主要研究方向:模式识别及图像处理;通讯作者:赵化启(1973-),男,博士,教授,主要研究方向:人工智能、计算机控制。Email:zhaohuaqi@126.com。
