Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2017-02-21T00:17:08Z
No. of bitstreams: 1
BrennaKarolynaDosSantosSilva_DISSERT.pdf: 6981280 bytes, checksum: 0266d5d98ab38bb268fe5ddd9e7e3c97 (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2017-03-03T22:08:40Z (GMT) No. of bitstreams: 1
BrennaKarolynaDosSantosSilva_DISSERT.pdf: 6981280 bytes, checksum: 0266d5d98ab38bb268fe5ddd9e7e3c97 (MD5) / Made available in DSpace on 2017-03-03T22:08:40Z (GMT). No. of bitstreams: 1
BrennaKarolynaDosSantosSilva_DISSERT.pdf: 6981280 bytes, checksum: 0266d5d98ab38bb268fe5ddd9e7e3c97 (MD5)
Previous issue date: 2016-02-23 / A identifica??o de sistemas din?micos n?o lineares tem grande import?ncia em v?rias
?reas da Engenharia. Esta disserta??o apresenta uma t?cnica de identifica??o de sistemas
utilizando Redes Neurais Artificiais do tipo Fun??es de Base Radial ? RBF aplicada a um
sistema bastante comum na ind?stria de qu?mica e petroqu?mica, o sistema de multitanques
(tanques acoplados), que apresenta uma din?mica de natureza n?o linear. Em tal sistema, o
comportamento n?o linear deste pode dificultar a identifica??o e apresentar um modelo que n?o
reflete seu comportamento real. Esse fator ? fonte de motiva??o para utilizarmos uma
ferramenta matem?tica tal como a RBF na identifica??o dos aspectos n?o lineares do sistema.
Essa rede apresenta potencialidades como habilidade de tratar sistemas complexos, aprendizado
e generaliza??o. A identifica??o de sistemas utilizando RBF pode ser tratada como um
problema de ajuste de curvas atrav?s da combina??o das fun??es de base. / The identification of nonlinear dynamical systems is of great importance in many areas
of engineering. In this dissertation presents a system identification technique using Artificial
Neural Networks Radial Basis Function type - RBF applied to a very common system in the
chemical and petrochemical industry, multi tanks system (coupled tanks), which features a
dynamic nature not linear. In such a system, the nonlinear behavior of this can make it difficult
to identify and present a model that does not reflect their actual behavior. This factor is a source
of motivation we use a mathematical tool such as RBF in the identification of nonlinear aspects
of the system. This network has potential as ability to handle complex systems, learning and
generalization. Identification systems using RBF can be treated as a curve fitting problem by
combining the basic functions.
| Identifer | oai:union.ndltd.org:IBICT/oai:repositorio.ufrn.br:123456789/22129 |
| Date | 23 February 2016 |
| Creators | Silva, Brenna Karolyna dos Santos |
| Contributors | 42046637100, http://lattes.cnpq.br/0477027244297797, Ara?jo, F?bio Meneghetti Ugulino de, 82675090468, http://lattes.cnpq.br/5473196176458886, Gabriel Filho, Oscar, 11376040697, http://lattes.cnpq.br/4171033998524192, Maitelli, Andr? Laurindo |
| Publisher | PROGRAMA DE P?S-GRADUA??O EM CI?NCIA E ENGENHARIA DE PETR?LEO, UFRN, Brasil |
| Source Sets | IBICT Brazilian ETDs |
| Language | Portuguese |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/masterThesis |
| Source | reponame:Repositório Institucional da UFRN, instname:Universidade Federal do Rio Grande do Norte, instacron:UFRN |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0022 seconds