【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。更多还原

【Abstract】 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.更多还原