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An Improved Kernel Based Extreme Learning Machine for Robot Execution Failures

Robot execution failures prediction (classification) in the robot tasks is a difficult learning problem due to partially corrupted or incomplete measurements of data and unsuitable prediction techniques for this prediction problem with little learning samples. Therefore, how to predict the robot exe...

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Detalles Bibliográficos
Autores principales: Li, Bin, Rong, Xuewen, Li, Yibin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3996967/
https://www.ncbi.nlm.nih.gov/pubmed/24977234
http://dx.doi.org/10.1155/2014/906546
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author Li, Bin
Rong, Xuewen
Li, Yibin
author_facet Li, Bin
Rong, Xuewen
Li, Yibin
author_sort Li, Bin
collection PubMed
description Robot execution failures prediction (classification) in the robot tasks is a difficult learning problem due to partially corrupted or incomplete measurements of data and unsuitable prediction techniques for this prediction problem with little learning samples. Therefore, how to predict the robot execution failures problem with little (incomplete) or erroneous data deserves more attention in the robot field. For improving the prediction accuracy of robot execution failures, this paper proposes a novel KELM learning algorithm using the particle swarm optimization approach to optimize the parameters of kernel functions of neural networks, which is called the AKELM learning algorithm. The simulation results with the robot execution failures datasets show that, by optimizing the kernel parameters, the proposed algorithm has good generalization performance and outperforms KELM and the other approaches in terms of classification accuracy. Other benchmark problems simulation results also show the efficiency and effectiveness of the proposed algorithm.
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spelling pubmed-39969672014-06-29 An Improved Kernel Based Extreme Learning Machine for Robot Execution Failures Li, Bin Rong, Xuewen Li, Yibin ScientificWorldJournal Research Article Robot execution failures prediction (classification) in the robot tasks is a difficult learning problem due to partially corrupted or incomplete measurements of data and unsuitable prediction techniques for this prediction problem with little learning samples. Therefore, how to predict the robot execution failures problem with little (incomplete) or erroneous data deserves more attention in the robot field. For improving the prediction accuracy of robot execution failures, this paper proposes a novel KELM learning algorithm using the particle swarm optimization approach to optimize the parameters of kernel functions of neural networks, which is called the AKELM learning algorithm. The simulation results with the robot execution failures datasets show that, by optimizing the kernel parameters, the proposed algorithm has good generalization performance and outperforms KELM and the other approaches in terms of classification accuracy. Other benchmark problems simulation results also show the efficiency and effectiveness of the proposed algorithm. Hindawi Publishing Corporation 2014 2014-04-08 /pmc/articles/PMC3996967/ /pubmed/24977234 http://dx.doi.org/10.1155/2014/906546 Text en Copyright © 2014 Bin Li et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Li, Bin
Rong, Xuewen
Li, Yibin
An Improved Kernel Based Extreme Learning Machine for Robot Execution Failures
title An Improved Kernel Based Extreme Learning Machine for Robot Execution Failures
title_full An Improved Kernel Based Extreme Learning Machine for Robot Execution Failures
title_fullStr An Improved Kernel Based Extreme Learning Machine for Robot Execution Failures
title_full_unstemmed An Improved Kernel Based Extreme Learning Machine for Robot Execution Failures
title_short An Improved Kernel Based Extreme Learning Machine for Robot Execution Failures
title_sort improved kernel based extreme learning machine for robot execution failures
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3996967/
https://www.ncbi.nlm.nih.gov/pubmed/24977234
http://dx.doi.org/10.1155/2014/906546
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