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Mostrar registro sencillo 2008-05-22T19:30:53Z 2008-05-22T03:00:00Z 2005-04
dc.description.abstract This paper presents a neuro-fuzzy network where all its parameters can be tuned simultaneously using Genetic Algorithms. The approach combines the merits of fuzzy logic theory, neural networks and genetic algorithms. The proposed neuro-fuzzy network does not require a priori knowledge about the system and eliminates the need for complicated design steps like manual tuning of input-output membership functions, and selection of fuzzy rule base. Although, only conventional genetic algorithms have been used, convergence results are very encouraging. A well known numerical example derived from literature is used to evaluate and compare the performance of the network with other modelling approaches. The network is further implemented as controller for two simulated thermal processes and their performances are compared with other existing controllers. Simulation results show that the proposed neuro-fuzzy controller whose all parameters have been tuned simultaneously using GAs, offers advantages over existing controllers and has improved performance. en
dc.format.extent p. 37-43 es
dc.language en es
dc.title A genetic based neuro-fuzzy controller for thermal processes en
dc.type Articulo es
sedici.identifier.uri es
sedici.identifier.issn 1666-6038 es
sedici.creator.person Goel, Ashok Kumar es
sedici.creator.person Saxena Chandra, Suresh es
sedici.creator.person Bhanot, Surekha es
sedici.subject.materias Ciencias Informáticas es
sedici.subject.other fuzzy logic en
sedici.description.fulltext true es Facultad de Informática es
sedici.subtype Articulo es
sedici.rights.license Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
sedici.description.peerReview peer-review es
sedici2003.identifier ARG-UNLP-ART-0000000597 es
sedici.relation.journalTitle Journal of Computer Science & Technology es
sedici.relation.journalVolumeAndIssue vol. 5, no. 1 es
sedici.subject.acmcss98 Neural nets es
sedici.subject.acmcss98 Algorithms es

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Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0) Excepto donde se diga explícitamente, este item se publica bajo la siguiente licencia Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)