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dc.contributor.authorLiu, Yen
dc.contributor.authorTeng, Zen
dc.contributor.authorWang, Gen
dc.contributor.authorLi, Cen
dc.contributor.authorLiu, Ken
dc.contributor.authorSun, Zen

In this paper, the adaptive fuzzy neural network (AFNN) based on the surface electromyography (sEMG) for estimating the elbow joint angle is established and investigated from the perspective of rapidity and accuracy. In addition, back propagation neural network (BPNN) and artificial neural network of radial basis function (RBFNN), as the classical method for data forecasting, have been applied to estimate the elbow joint angle for comparing with AFNN. Ultimately, the experimental simulation and result analysis demonstrate that the rapidity and accuracy of AFNN is superior to BPNN and RBFNN.

dc.format.extent1091 - 1096en
dc.titleIntention recognition of elbow joint based on sEMG using adaptive fuzzy neural networken
dc.typeConference Contribution
plymouth.journalProceedings - 2020 5th International Conference on Mechanical, Control and Computer Engineering, ICMCCE 2020en
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dc.rights.embargoperiodNot knownen
rioxxterms.typeConference Paper/Proceeding/Abstracten

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