Conditioning of extreme learning machine for noisy data using heuristic optimization
This article provides a tool that can be used in the exact sciences to obtain good approximations to reality when noisy data is inevitable. Two heuristic optimization algorithms are implemented: Simulated Annealing and Particle Swarming for the determination of the extreme learning machine output we...
- Autores:
-
Salazar, E
Mora, M
Vásquez, A
Gelvez, E
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad Simón Bolívar
- Repositorio:
- Repositorio Digital USB
- Idioma:
- eng
- OAI Identifier:
- oai:bonga.unisimon.edu.co:20.500.12442/6381
- Acceso en línea:
- https://hdl.handle.net/20.500.12442/6381
https://iopscience.iop.org/article/10.1088/1742-6596/1514/1/012007/pdf
- Palabra clave:
- Exact sciences
Data
Optimization algorithms
Heuristic algorithms
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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Conditioning of extreme learning machine for noisy data using heuristic optimization |
| title |
Conditioning of extreme learning machine for noisy data using heuristic optimization |
| spellingShingle |
Conditioning of extreme learning machine for noisy data using heuristic optimization Exact sciences Data Optimization algorithms Heuristic algorithms |
| title_short |
Conditioning of extreme learning machine for noisy data using heuristic optimization |
| title_full |
Conditioning of extreme learning machine for noisy data using heuristic optimization |
| title_fullStr |
Conditioning of extreme learning machine for noisy data using heuristic optimization |
| title_full_unstemmed |
Conditioning of extreme learning machine for noisy data using heuristic optimization |
| title_sort |
Conditioning of extreme learning machine for noisy data using heuristic optimization |
| dc.creator.fl_str_mv |
Salazar, E Mora, M Vásquez, A Gelvez, E |
| dc.contributor.author.none.fl_str_mv |
Salazar, E Mora, M Vásquez, A Gelvez, E |
| dc.subject.eng.fl_str_mv |
Exact sciences Data Optimization algorithms Heuristic algorithms |
| topic |
Exact sciences Data Optimization algorithms Heuristic algorithms |
| description |
This article provides a tool that can be used in the exact sciences to obtain good approximations to reality when noisy data is inevitable. Two heuristic optimization algorithms are implemented: Simulated Annealing and Particle Swarming for the determination of the extreme learning machine output weights. The first operates in a large search space and at each iteration it probabilistically decides between staying at its current state or moving to another. The swarm of particles, it optimizes a problem from a population of candidate solutions, moving them throughout the search space according to position and speed. The methodology consists of building data sets around a polynomial function, implementing the heuristic algorithms and comparing the errors with the traditional computation method using the Moore–Penrose inverse. The results show that the heuristic optimization algorithms implemented improve the estimation of the output weights when the input have highly noisy data. |
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2020 |
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2020-08-27T23:53:04Z |
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2020-08-27T23:53:04Z |
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2020 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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17426588 |
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https://hdl.handle.net/20.500.12442/6381 |
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https://iopscience.iop.org/article/10.1088/1742-6596/1514/1/012007/pdf |
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17426588 |
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https://hdl.handle.net/20.500.12442/6381 https://iopscience.iop.org/article/10.1088/1742-6596/1514/1/012007/pdf |
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eng |
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eng |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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pdf |
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IOP Publishing |
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Journal of Physics: Conference Series |
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Vol. 1514 No. 1 (2020) |
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Universidad Simón Bolívar |
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Salazar, E986e187f-0d67-43f5-81cd-ec0b48cc4766Mora, M630644e5-97cc-482b-b5d0-8f65851e3399Vásquez, Ad8f4cb05-84e7-4272-9e5f-5744834df75fGelvez, Ed34b29f4-5323-4e58-83ca-7ae2e85e1ce02020-08-27T23:53:04Z2020-08-27T23:53:04Z202017426588https://hdl.handle.net/20.500.12442/6381https://iopscience.iop.org/article/10.1088/1742-6596/1514/1/012007/pdfThis article provides a tool that can be used in the exact sciences to obtain good approximations to reality when noisy data is inevitable. Two heuristic optimization algorithms are implemented: Simulated Annealing and Particle Swarming for the determination of the extreme learning machine output weights. The first operates in a large search space and at each iteration it probabilistically decides between staying at its current state or moving to another. The swarm of particles, it optimizes a problem from a population of candidate solutions, moving them throughout the search space according to position and speed. The methodology consists of building data sets around a polynomial function, implementing the heuristic algorithms and comparing the errors with the traditional computation method using the Moore–Penrose inverse. The results show that the heuristic optimization algorithms implemented improve the estimation of the output weights when the input have highly noisy data.pdfengIOP PublishingAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Journal of Physics: Conference SeriesVol. 1514 No. 1 (2020)Exact sciencesDataOptimization algorithmsHeuristic algorithmsConditioning of extreme learning machine for noisy data using heuristic optimizationinfo:eu-repo/semantics/articleArtículo científicohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Varela E and Campbells E 2011 Redes Neuronales Artificiales: Una revisión del estado del arte, aplicaciones y tendencias futuras Revista Investigación y Desarrollo en TIC 2 18Karayiannis N and Venetsanopoulos A 2013 Artificial Neural Networks (New York: Springer Science+Business Media) Learning algorithms, performance evaluation, and applications 209Hornik K, Stinchcombe M and White H 1989 Multilayer feedforward networks are universal approximators Neural Networks 2 359Huang G B, Chen L and Siew C K 2006 Universal approximation using incremental constructive feedforward networks with random hidden nodes IEEE Trans. Neural Networks 17 879Zhang L, Zhang D and Tian F 2016 SVM and ELM: Who Wins? Object recognition with deep convolutional features from ImageNet Proceedings of ELM-2015 1 249Huang G B and Chen L 2008 Enhanced random search based incremental extreme learning machine Neurocomputing 71 3460Yang Y, Wang Y and Yuan X 2012 Bidirectional extreme learning machine for regression problem and its learning effectiveness IEEE Transactions on Neural Networks and Learning Systems 23 1498Cao W, Ming Z, Wang X and Cai S 2019 Improved bidirectional extreme learning machine based on enhanced random search Memetic Computing 11 19Miche Y, Sorjamaa A, Bas P, Simula O, Jutten C and Lendasse A 2009 OP-ELM: Optimally pruned extreme learning machine IEEE transactions on Neural Networks 21 158Ranganathan S, Nakai K and Schonbach C 2018 Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics (Cambridge: Elsevier)Khachaturyan A, Semenovskaya S and Vainshtein B 1979 Statistical-thermodynamic approach to determination of structure amplitude phases Sov. Phys. Crystallography 24 519Kirkpatrick S, Gelatt J and Vecchi 1983 Optimization by simulated annealing Science 220 671Semenovskaya S V, Khachaturyan K A and Khachaturyan A G 1985 Statistical mechanics approach to the determination of a crystal Acta Cryst. A41 268Brownlee J 2011 Clever algorithms: Nature-inspired Programming Recipes (Autralia: Jason Brownlee)Eberhart R and Kennedy J 1995 Proceedings of ICNN'95 - International Conference on Neural Networks (Perth: IEEE) Particle swarm optimization 1942Eberhart R, Shi Y and Kennedy J 2001 Swarm Intelligence (San Diego: Academic Press)CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://bonga.unisimon.edu.co/bitstreams/1c98716b-cceb-4396-9a85-71ddf40b31d4/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-8381https://bonga.unisimon.edu.co/bitstreams/971df614-3603-450b-8f1e-c406873da968/download733bec43a0bf5ade4d97db708e29b185MD53ORIGINALPDF.pdfPDF.pdfPDFapplication/pdf1070260https://bonga.unisimon.edu.co/bitstreams/cf62b938-4caa-44e1-9a46-97bb1af6f830/download79945951028744daddb924d373ce3da8MD51TEXTPDF.pdf.txtPDF.pdf.txtExtracted texttext/plain20791https://bonga.unisimon.edu.co/bitstreams/203c9ea4-3494-471d-ab64-92a23541abf2/download1ff401975ce33134573c2facd5454bd6MD54THUMBNAILPDF.pdf.jpgPDF.pdf.jpgGenerated Thumbnailimage/jpeg1398https://bonga.unisimon.edu.co/bitstreams/ccff8517-f1d3-400c-a615-c06eec44a2dd/download296d6e7bdba49a87841fd0e29a797791MD5520.500.12442/6381oai:bonga.unisimon.edu.co:20.500.12442/63812024-08-14 21:51:54.56http://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internacionalopen.accesshttps://bonga.unisimon.edu.coRepositorio Digital Universidad Simón Bolívarrepositorio.digital@unisimon.edu.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 |