1004-6801 2019 39 6 1225 1231 10.16450/j.cnki.issn.1004-6801.2019.06.012 article Locomotion-Mode Recognition Based on Fish Swarm Algorithm and Extreme Learning Machine 为提高下肢假肢步态识别的准确性，提出一种基于鱼群(fish swarm ，简称FA)算法优化极限学习机(extreme learning machine，简称 ELM)的模式识别方法。首先，提取张量投影特征，分析了特征值选取的合理性；其次，采用主成分分析法降维；最后，利用鱼群算法进化极限学习机分类识别平地行走、上楼、下楼、上坡及下坡5种步态，识别准确率达到 97.45%。通过实验比较了该算法与极限学习机等分类器在假肢步态分类上的识别准确率与识别时间，结果表明，FA-ELM方法识别准确率优于其他方法。 In light of the accuracy and timeliness of locomotion-mode recognition of intelligent prosthesis, a pattern recognition method based on the fish swarm (FA) algorithm is proposed to optimize the extreme learning machine (ELM). First, the features of tensor projection are extracted, and the rationality of the selection of features is analyzed. Then, principal component analysis is used to reduce the dimension. Finally, treads of walking on-ground, upstairs, downstairs, uphill, and downhill are recognized using the learning machine of FA-ELM. The recognition accuracy is 97.45%. The comparison with ELM and other classifiers shows that FA-ELM recognizes more accurately. 表面肌电信号；极限学习机；鱼群算法；步态识别；智能假肢 Surface electromyography(sEMG);extreme learning machine(ELM);fish swarm algorithm(FA); locomotion-mode recognition; intelligent prosthesis 刘磊,陈增强,杨鹏,刘作军 LIU Lei,CHEN Zengqiang,YANG Peng,LIU Zuojun http://zdcs.nuaa.edu.cn/zdcsyzd/article/abstract/201906012