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Conference Paper: Nonlinear static decoupling of six-dimension force sensor for walker dynamometer systembased on artificial neural network

TitleNonlinear static decoupling of six-dimension force sensor for walker dynamometer systembased on artificial neural network
Authors
KeywordsBack Propagation Neural Network
Radial Basis Function Neural Network
Static Coupling
Walker
Issue Date2009
Citation
2009 Ieee International Conference On Computational Intelligence For Measurement Systems And Applications, Cimsa 2009, 2009, p. 14-17 How to Cite?
AbstractThe static coupling of six-dimension force sensor for walker dynamometer system is a key factor to limit its measuring precision. A new decoupling method based on artificial neural network is proposed in this paper. Relevant error check results shows that, after the calibration by using the Back Propagation neural network and Radial Basis Function neural networks, the maximal system precision error with single-direction force was 7.78% and 4.33% and the maximal crosstalk was 7.49% and 6.52%, respectively. In comparison with traditional linear calibration method, the proposed technique can effectively increase the measurement accuracy of walker loads and greatly decrease the coupling effect. © 2009 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/173420
ISBN
ISSN
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorMing, Den_US
dc.contributor.authorZhang, Xen_US
dc.contributor.authorLiu, Xen_US
dc.contributor.authorWan, Ben_US
dc.contributor.authorHu, Yen_US
dc.contributor.authorLuk, KDKen_US
dc.date.accessioned2012-10-30T06:31:00Z-
dc.date.available2012-10-30T06:31:00Z-
dc.date.issued2009en_US
dc.identifier.citation2009 Ieee International Conference On Computational Intelligence For Measurement Systems And Applications, Cimsa 2009, 2009, p. 14-17en_US
dc.identifier.isbn978-1-4244-3819-8-
dc.identifier.issn2159-1547-
dc.identifier.urihttp://hdl.handle.net/10722/173420-
dc.description.abstractThe static coupling of six-dimension force sensor for walker dynamometer system is a key factor to limit its measuring precision. A new decoupling method based on artificial neural network is proposed in this paper. Relevant error check results shows that, after the calibration by using the Back Propagation neural network and Radial Basis Function neural networks, the maximal system precision error with single-direction force was 7.78% and 4.33% and the maximal crosstalk was 7.49% and 6.52%, respectively. In comparison with traditional linear calibration method, the proposed technique can effectively increase the measurement accuracy of walker loads and greatly decrease the coupling effect. © 2009 IEEE.en_US
dc.languageengen_US
dc.relation.ispartof2009 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2009en_US
dc.subjectBack Propagation Neural Networken_US
dc.subjectRadial Basis Function Neural Networken_US
dc.subjectStatic Couplingen_US
dc.subjectWalkeren_US
dc.titleNonlinear static decoupling of six-dimension force sensor for walker dynamometer systembased on artificial neural networken_US
dc.typeConference_Paperen_US
dc.identifier.emailHu, Y:yhud@hku.hken_US
dc.identifier.emailLuk, KDK:hcm21000@hku.hken_US
dc.identifier.authorityHu, Y=rp00432en_US
dc.identifier.authorityLuk, KDK=rp00333en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1109/CIMSA.2009.5069909en_US
dc.identifier.scopuseid_2-s2.0-77950838069en_US
dc.identifier.hkuros159910-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77950838069&selection=ref&src=s&origin=recordpageen_US
dc.identifier.spage14en_US
dc.identifier.epage17en_US
dc.identifier.isiWOS:000270710800004-
dc.identifier.scopusauthoridMing, D=9745824400en_US
dc.identifier.scopusauthoridZhang, X=23986626500en_US
dc.identifier.scopusauthoridLiu, X=35109400600en_US
dc.identifier.scopusauthoridWan, B=7102316798en_US
dc.identifier.scopusauthoridHu, Y=7407116091en_US
dc.identifier.scopusauthoridLuk, KDK=7201921573en_US

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