Please use this identifier to cite or link to this item: https://doi.org/10.1145/2185677.2185697
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dc.titleExtreme learning machine for wireless indoor localization
dc.contributor.authorXiao, W.
dc.contributor.authorLiu, P.
dc.contributor.authorSoh, W.-S.
dc.contributor.authorJin, Y.
dc.date.accessioned2014-06-19T03:10:12Z
dc.date.available2014-06-19T03:10:12Z
dc.date.issued2012
dc.identifier.citationXiao, W.,Liu, P.,Soh, W.-S.,Jin, Y. (2012). Extreme learning machine for wireless indoor localization. IPSN'12 - Proceedings of the 11th International Conference on Information Processing in Sensor Networks : 101-102. ScholarBank@NUS Repository. <a href="https://doi.org/10.1145/2185677.2185697" target="_blank">https://doi.org/10.1145/2185677.2185697</a>
dc.identifier.isbn9781450312271
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/70271
dc.description.abstractDue to the widespread deployment and low cost, WLAN has drawn much attention for indoor localization. In this poster, an efficient indoor localization algorithm, which utilizes the WLAN received signal strength from each Access Point (AP), has been proposed. The algorithm is based on the Extreme Learning Machine (ELM), a Single layer Feed-forward neural Network (SLFN). It is competitive fast in offline learning and online localization. Also, compared with existing fingerprinting approach, it does not need the fingerprinting database in the online phase, which can substantially reduce the required storage space of the terminal devices. © 2012 ACM.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1145/2185677.2185697
dc.sourceScopus
dc.subjectELM
dc.subjectFingerprinting
dc.subjectIndoor localization
dc.subjectNeural network
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1145/2185677.2185697
dc.description.sourcetitleIPSN'12 - Proceedings of the 11th International Conference on Information Processing in Sensor Networks
dc.description.page101-102
dc.identifier.isiutNOT_IN_WOS
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