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...
Main Authors: | , , , |
---|---|
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 |
_version_ | 1827079699671547904 |
---|---|
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. |
first_indexed | 2024-03-12T10:27:49Z |
format | Article |
id | doaj.art-6481359cd6d44082ab6c9710f530c3b2 |
institution | Directory Open Access Journal |
issn | 1550-1477 |
language | English |
last_indexed | 2025-03-20T02:49:43Z |
publishDate | 2017-06-01 |
publisher | Hindawi - SAGE Publishing |
record_format | Article |
series | International Journal of Distributed Sensor Networks |
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 |