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

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

doi:10.1109/ICARCV.2014.7064376

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 proceeding › Conference 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 language	English
Title of host publication	2014 13th International Conference on Control Automation Robotics Vision (ICARCV)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	625-630
Number of pages	6
ISBN (Electronic)	978-1-4799-5199-4
DOIs	https://doi.org/10.1109/ICARCV.2014.7064376
Publication status	Published - 12 Oct 2014
Event	13th International Conference on Control, Automation, Robotics and Vision - , Singapore Duration: 10 Dec 2014 → 12 Dec 2014

Conference

Conference	13th International Conference on Control, Automation, Robotics and Vision
Abbreviated title	ICARCV 2014
Country/Territory	Singapore
Period	10/12/14 → 12/12/14

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https://ieeexplore.ieee.org/document/7064376Licence: All Rights Reserved

Cite this

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title = "Extreme learning machine with dead zone and its application to WiFi based indoor positioning",

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.",

author = "Xiaoxuan Lu and Chengpu Yu and Han Zou and Hao Jiang and Lihua Xie",

year = "2014",

month = oct,

day = "12",

doi = "10.1109/ICARCV.2014.7064376",

language = "English",

pages = "625--630",

booktitle = "2014 13th International Conference on Control Automation Robotics Vision (ICARCV)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "13th International Conference on Control, Automation, Robotics and Vision, ICARCV 2014 ; Conference date: 10-12-2014 Through 12-12-2014",

}

Lu, X, Yu, C, Zou, H, Jiang, H & Xie, L 2014, Extreme learning machine with dead zone and its application to WiFi based indoor positioning. in 2014 13th International Conference on Control Automation Robotics Vision (ICARCV). Institute of Electrical and Electronics Engineers (IEEE), pp. 625-630, 13th International Conference on Control, Automation, Robotics and Vision, Singapore, 10/12/14. https://doi.org/10.1109/ICARCV.2014.7064376

Extreme learning machine with dead zone and its application to WiFi based indoor positioning. / Lu, Xiaoxuan; Yu, Chengpu; Zou, Han; Jiang, Hao; Xie, Lihua.

2014 13th International Conference on Control Automation Robotics Vision (ICARCV). Institute of Electrical and Electronics Engineers (IEEE), 2014. p. 625-630.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Extreme learning machine with dead zone and its application to WiFi based indoor positioning

AU - Lu, Xiaoxuan

AU - Yu, Chengpu

AU - Zou, Han

AU - Jiang, Hao

AU - Xie, Lihua

PY - 2014/10/12

Y1 - 2014/10/12

N2 - 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.

AB - 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.

U2 - 10.1109/ICARCV.2014.7064376

DO - 10.1109/ICARCV.2014.7064376

M3 - Conference contribution

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BT - 2014 13th International Conference on Control Automation Robotics Vision (ICARCV)

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 13th International Conference on Control, Automation, Robotics and Vision

Y2 - 10 December 2014 through 12 December 2014

ER -

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

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