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dc.contributor.authorRodríguez Cobo, Luis 
dc.contributor.authorMirapeix Serrano, Jesús María 
dc.contributor.authorCobo García, Adolfo 
dc.contributor.authorLópez Higuera, José Miguel 
dc.contributor.otherUniversidad de Cantabriaes_ES
dc.date.accessioned2016-01-27T08:45:24Z
dc.date.available2016-01-27T08:45:24Z
dc.date.issued2015
dc.identifier.issn1045-389X
dc.identifier.issn1530-8138
dc.identifier.otherTEC2010-20224-C02-02es_ES
dc.identifier.urihttp://hdl.handle.net/10902/7968
dc.description.abstractThe ability of two different machine learning approaches to map non-linear problems from experimental data is evaluated under controlled experiments. A well-known machine learning algorithm (Artificial Neural Network) is compared against a new computing paradigm (Hierarchical Temporal Memory) under a controlled scenario. The chosen scenario is the detection of impacts in a cantilever beam under vibration instrumented with fiber Bragg gratings. The main characteristics of both of the machine learning approaches are analyzed while varying environmental parameters such as the number of sensing points and their location. From the achieved results some clues can be extracted regarding dealing with noisy or partial data using different machine learning approaches.es_ES
dc.format.extent15 p.es_ES
dc.language.isoenges_ES
dc.publisherSAGE Publications Ltdes_ES
dc.rights© SAGE. The final, definitive version of this paper has been published in [Journal of Intelligent Material Systems], Vol 26/Issue 10, July/2015 published by SAGE Publishing, All rights reserved.es_ES
dc.sourceJournal of Intelligent Material Systems 2015, Vol. 26(10) 1243–1250es_ES
dc.titleComparison of hierarchical temporal memories and artificial neural networks under noisy dataes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherVersionhttps://doi.org/10.1177/1045389X14538537es_ES
dc.rights.accessRightsopenAccesses_ES
dc.identifier.DOI10.1177/1045389X14538537
dc.type.versionacceptedVersiones_ES


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