UM
Interval type-2 fuzzy weighted support vector machine learning for energy efficient biped walking
Wang L.2; Liu Z.2; Chen C.L.P.3; Zhang Y.2
2014
Source PublicationApplied Intelligence
ISSN0924669X
Volume40Issue:3Pages:453-463
AbstractAn interval type-2 fuzzy weighted support vector machine (IT2FW-SVM) is proposed to address the problem of high energy consumption for biped walking robots. Different from the traditional machine learning method of 'copy learning', the proposed IT2FW-SVM obtains lower energy cost and larger zero moment point (ZMP) stability margin using a novel strategy of 'selective learning', which is similar to human selections based on experience. To handle the uncertainty of the experience, the learning weights in the IT2FW-SVM are deduced using an interval type-2 fuzzy logic system (IT2FLS), which is an extension of the previous weighted SVM. Simulation studies show that the existing biped walking which generates the original walking samples is improved remarkably in terms of both energy efficiency and biped dynamic balance using the proposed IT2FW-SVM. © 2013 Springer Science+Business Media New York.
KeywordBiped robot Energy cost Fuzzy logic system Support vector machine
DOI10.1007/s10489-013-0472-2
URLView the original
Language英語
Fulltext Access
Citation statistics
Cited Times [WOS]:4   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.Shunde Polytechnic
2.Guangdong University of Technology
3.Universidade de Macau
Recommended Citation
GB/T 7714
Wang L.,Liu Z.,Chen C.L.P.,et al. Interval type-2 fuzzy weighted support vector machine learning for energy efficient biped walking[J]. Applied Intelligence,2014,40(3):453-463.
APA Wang L.,Liu Z.,Chen C.L.P.,&Zhang Y..(2014).Interval type-2 fuzzy weighted support vector machine learning for energy efficient biped walking.Applied Intelligence,40(3),453-463.
MLA Wang L.,et al."Interval type-2 fuzzy weighted support vector machine learning for energy efficient biped walking".Applied Intelligence 40.3(2014):453-463.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wang L.]'s Articles
[Liu Z.]'s Articles
[Chen C.L.P.]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wang L.]'s Articles
[Liu Z.]'s Articles
[Chen C.L.P.]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang L.]'s Articles
[Liu Z.]'s Articles
[Chen C.L.P.]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.