摘要
针对基于参数寻优的支持向量机(SVM)方法存在早熟收敛、全局寻优能力差、局部寻优精度低等问题,提出一种自适应粒子群/布谷鸟(APSO/CS)参数寻优方法,旨在实现SVM模型中核函数参数、惩罚因子的优化。测试函数分别对APSO/CS、APSO、CS的参数寻优性能进行了对比分析,表明APSO/CS寻优能够加快局部和全局寻优的收敛速度。采用表面肌电信号(sEMG)对APSO/CS、APSO、CS寻优的SVM方法进行了手势识别对比测试,实验测试结果表明,采用APSO/CS寻优的SVM方法进行手势识别时正确率最高,最高正确率可达94.50%,该方法可为识别分类算法提供一种新思路。
Aiming at the problems of premature convergence, poor ability in global optimization and low accuracy in local optimization for support vector machine(SVM) based on parameter optimization, an adaptive particle swarm optimization/cuckoo(APSO/CS) parameter optimization method is proposed, in which the optimization of kernel function parameters and penalty factors in SVM model is realized. The optimization performance of APSO/CS, APSO and CS is compared and analyzed by test functions, which shows that APSO/CS can accelerate the convergence speed of local and global optimization. The gesture recognition methods based on surface electromyography signal(sEMG) by APSO/CS, APSO and CS are compared. The experiment results show that the SVM method optimized by APSO/CS can realize the highest recognition accuracy, which is about 94.50%. The proposed method can provide a new way for the recognition and classification algorithm.
作者
徐云
王福能
Xu Yun;Wang Funeng(School of Mechanical Engineering&Automation,Zhejiang Sci-Tech University,Hangzhou 310098,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2020年第7期1-7,共7页
Journal of Electronic Measurement and Instrumentation
基金
浙江省自然科学基金(LQ20F030019)资助项目。
