Hybrid particle swarm optimization and semi-supervised extreme learning machine for cellular network localization

The research of localization technology based on received signal strength and machine learning has recently attracted a lot of attentions, since with the help of enough labeled training data this technology is able to achieve high positioning accuracy. However, it is an expensive job to collect enou...

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Main Authors: Fagui Liu, Hengrui Qin, Xin Yang, Yi Yu
Format: Article
Language:English
Published: Hindawi - SAGE Publishing 2017-06-01
Series:International Journal of Distributed Sensor Networks
Online Access:https://doi.org/10.1177/1550147717717190
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author Fagui Liu
Hengrui Qin
Xin Yang
Yi Yu
author_facet Fagui Liu
Hengrui Qin
Xin Yang
Yi Yu
author_sort Fagui Liu
collection DOAJ
description The research of localization technology based on received signal strength and machine learning has recently attracted a lot of attentions, since with the help of enough labeled training data this technology is able to achieve high positioning accuracy. However, it is an expensive job to collect enough labeled training data in the broad outdoor space. In order to reduce the cost of building and maintaining training database, semi-supervised extreme learning machine is applied to solve the cellular network localization in this article. However, the performance of this algorithm is sensitive to the values of the hyper parameters. Without any systematic guidance, the optimal hyper parameters can only be selected by experienced workers through trial and error. To address this problem, we propose a novel algorithm by combining particle swarm optimization and semi-supervised extreme learning machine to automatically select the optimal hyper parameters of semi-supervised extreme learning machine in this article. The experiments demonstrate that applying particle swarm optimization in our optimization framework makes the hyper parameters of semi-supervised extreme learning machine algorithm self-adaptive in different conditions. Moreover, the proposed method is more stable than the general semi-supervised extreme learning machine and outperforms other compared methods.
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spelling doaj.art-6481359cd6d44082ab6c9710f530c3b22024-10-03T07:27:39ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772017-06-011310.1177/1550147717717190Hybrid particle swarm optimization and semi-supervised extreme learning machine for cellular network localizationFagui Liu0Hengrui Qin1Xin Yang2Yi Yu3School of Computer Science and Engineering, South China University of Technology, Guangzhou, ChinaSchool of Computer Science and Engineering, South China University of Technology, Guangzhou, ChinaSchool of Computer Science and Engineering, South China University of Technology, Guangzhou, ChinaGCI Science & Technology Co. Ltd., Guangzhou, ChinaThe research of localization technology based on received signal strength and machine learning has recently attracted a lot of attentions, since with the help of enough labeled training data this technology is able to achieve high positioning accuracy. However, it is an expensive job to collect enough labeled training data in the broad outdoor space. In order to reduce the cost of building and maintaining training database, semi-supervised extreme learning machine is applied to solve the cellular network localization in this article. However, the performance of this algorithm is sensitive to the values of the hyper parameters. Without any systematic guidance, the optimal hyper parameters can only be selected by experienced workers through trial and error. To address this problem, we propose a novel algorithm by combining particle swarm optimization and semi-supervised extreme learning machine to automatically select the optimal hyper parameters of semi-supervised extreme learning machine in this article. The experiments demonstrate that applying particle swarm optimization in our optimization framework makes the hyper parameters of semi-supervised extreme learning machine algorithm self-adaptive in different conditions. Moreover, the proposed method is more stable than the general semi-supervised extreme learning machine and outperforms other compared methods.https://doi.org/10.1177/1550147717717190
spellingShingle Fagui Liu
Hengrui Qin
Xin Yang
Yi Yu
Hybrid particle swarm optimization and semi-supervised extreme learning machine for cellular network localization
International Journal of Distributed Sensor Networks
title Hybrid particle swarm optimization and semi-supervised extreme learning machine for cellular network localization
title_full Hybrid particle swarm optimization and semi-supervised extreme learning machine for cellular network localization
title_fullStr Hybrid particle swarm optimization and semi-supervised extreme learning machine for cellular network localization
title_full_unstemmed Hybrid particle swarm optimization and semi-supervised extreme learning machine for cellular network localization
title_short Hybrid particle swarm optimization and semi-supervised extreme learning machine for cellular network localization
title_sort hybrid particle swarm optimization and semi supervised extreme learning machine for cellular network localization
url https://doi.org/10.1177/1550147717717190
work_keys_str_mv AT faguiliu hybridparticleswarmoptimizationandsemisupervisedextremelearningmachineforcellularnetworklocalization
AT hengruiqin hybridparticleswarmoptimizationandsemisupervisedextremelearningmachineforcellularnetworklocalization
AT xinyang hybridparticleswarmoptimizationandsemisupervisedextremelearningmachineforcellularnetworklocalization
AT yiyu hybridparticleswarmoptimizationandsemisupervisedextremelearningmachineforcellularnetworklocalization