Extreme learning machine with dead zone and its application to WiFi based indoor positioning

Xiaoxuan Lu, Chengpu Yu, Han Zou, Hao Jiang, Lihua Xie

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Extreme learning machine (ELM) as an emergent technology has shown its good performance in regression applications as well as in large dataset classification applications. It has been broadly embedded in many applications due to its fast speed of computation and accuracy. How to make good use of machine learning techniques in Indoor Positioning System (IPS) is a hot research topic in recent years. Some existing IPSs have already adopted ELM, but it suffers from signal variation and environmental dynamics in indoor settings. In this paper, extreme learning machine with dead zone (DZ-ELM) is proposed to address this problem. The consistency of this approach should be applied is studied. Simulations are also conducted to compare the performance of DZ-ELM and ELM. Lastly, real-world experimental results show that the proposed algorithm can not only provide higher accuracy but also improve the repeatability of IPSs.
Original languageEnglish
Title of host publication2014 13th International Conference on Control Automation Robotics Vision (ICARCV)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages625-630
Number of pages6
ISBN (Electronic)978-1-4799-5199-4
DOIs
Publication statusPublished - 12 Oct 2014
Event13th International Conference on Control, Automation, Robotics and Vision - , Singapore
Duration: 10 Dec 201412 Dec 2014

Conference

Conference13th International Conference on Control, Automation, Robotics and Vision
Abbreviated titleICARCV 2014
Country/TerritorySingapore
Period10/12/1412/12/14

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