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New possibilistic method for discovering linear local behavior using hyper-Gaussian distributed membership function

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New possibilistic method for discovering linear local behavior using hyper-Gaussian distributed membership function

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Nombre: Barcelo-Rico;Diez ...
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dc.contributor.author Barcelo-Rico, F. es_ES
dc.contributor.author Diez, José-Luís es_ES
dc.contributor.author Bondia Company, Jorge es_ES
dc.date.accessioned 2020-10-09T03:31:10Z
dc.date.available 2020-10-09T03:31:10Z
dc.date.issued 2012-02 es_ES
dc.identifier.issn 0219-1377 es_ES
dc.identifier.uri http://hdl.handle.net/10251/151435
dc.description.abstract [EN] This paper presents a method to find a model of a system based on the integration of a set of local models. Mainly, properties are sought for the local models: independence of clusters and interpretability of their validity. This has been achieved through the introduction of a possibilistic clustering for the first property and a pre-fixed shape of the membership functions for the second one. A new cost index for the clustering optimization problem has been defined consisting of two terms: one for global error and another for local errors. By giving higher importance to the local errors term, local models valid regionally can be found. To avoid local optima and numerical issues, the parameters of the models are found using global optimization. This new method has been applied to several data sets, and results show how the desired characteristics can be achieved in the resulting models. es_ES
dc.description.sponsorship The authors acknowledge the partial funding of this work by the projects: the national projects DPI2007-66728-C02-01 and DPI2008-06737-C02-01. The first author is recipient of a fellowship from the Spanish Ministry of Education (FPU AP2008-02967). The translation of this paper was funded by the Universidad Politecnica de Valencia, Spain. es_ES
dc.language Inglés es_ES
dc.publisher SPRINGER LONDON LTD es_ES
dc.relation.ispartof Knowledge and Information Systems es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Clustering es_ES
dc.subject Cost index es_ES
dc.subject Gaussian es_ES
dc.subject Global optimization es_ES
dc.subject Possibilistic es_ES
dc.subject.classification INGENIERIA DE SISTEMAS Y AUTOMATICA es_ES
dc.title New possibilistic method for discovering linear local behavior using hyper-Gaussian distributed membership function es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s10115-011-0385-5 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//DPI2008-06737-C02-01/ES/NUCLEO DE CONTROL EN SISTEMAS DISTRIBUIDOS/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MEC//DPI2007-66728-C02-01/ES/CONTROL DE GLUCEMIA EN LAZO CERRADO EN PACIENTES CON DIABETES MELLITUS 1 Y PACIENTES CRITICOS/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//AP2008-02967/ES/AP2008-02967/ es_ES
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería de Sistemas y Automática - Departament d'Enginyeria de Sistemes i Automàtica es_ES
dc.description.bibliographicCitation Barcelo-Rico, F.; Diez, J.; Bondia Company, J. (2012). New possibilistic method for discovering linear local behavior using hyper-Gaussian distributed membership function. Knowledge and Information Systems. 30(2):377-403. https://doi.org/10.1007/s10115-011-0385-5 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s10115-011-0385-5 es_ES
dc.description.upvformatpinicio 377 es_ES
dc.description.upvformatpfin 403 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 30 es_ES
dc.description.issue 2 es_ES
dc.relation.pasarela S\208959 es_ES
dc.contributor.funder Ministerio de Educación y Ciencia es_ES
dc.contributor.funder Universitat Politècnica de València es_ES
dc.contributor.funder Ministerio de Ciencia e Innovación
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