Made available in DSpace on 2014-12-17T14:55:19Z (GMT). No. of bitstreams: 1
JoseMAJ_TESE.pdf: 3560157 bytes, checksum: 2f20316c7b980a74bdb7b82e97e3bb43 (MD5)
Previous issue date: 2014-03-24 / Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico / In last decades, neural networks have been established as a major tool for the
identification of nonlinear systems. Among the various types of networks used in identification,
one that can be highlighted is the wavelet neural network (WNN). This network combines the
characteristics of wavelet multiresolution theory with learning ability and generalization of neural
networks usually, providing more accurate models than those ones obtained by traditional
networks. An extension of WNN networks is to combine the neuro-fuzzy ANFIS (Adaptive
Network Based Fuzzy Inference System) structure with wavelets, leading to generate the Fuzzy
Wavelet Neural Network - FWNN structure. This network is very similar to ANFIS networks,
with the difference that traditional polynomials present in consequent of this network are replaced
by WNN networks. This paper proposes the identification of nonlinear dynamical systems from a
network FWNN modified. In the proposed structure, functions only wavelets are used in the
consequent. Thus, it is possible to obtain a simplification of the structure, reducing the number of
adjustable parameters of the network. To evaluate the performance of network FWNN with this
modification, an analysis of network performance is made, verifying advantages, disadvantages
and cost effectiveness when compared to other existing FWNN structures in literature. The
evaluations are carried out via the identification of two simulated systems traditionally found in
the literature and a real nonlinear system, consisting of a nonlinear multi section tank. Finally, the
network is used to infer values of temperature and humidity inside of a neonatal incubator. The
execution of such analyzes is based on various criteria, like: mean squared error, number of
training epochs, number of adjustable parameters, the variation of the mean square error, among
others. The results found show the generalization ability of the modified structure, despite the
simplification performed / Nas ?ltimas d?cadas, as redes neurais t?m se estabelecido como uma das principais
ferramentas para a identifica??o de sistemas n?o lineares. Entre os diversos tipos de redes
utilizadas em identifica??o, uma que se pode destacar ? a rede neural wavelet (ou Wavelet Neural
Network - WNN). Esta rede combina as caracter?sticas de multirresolu??o da teoria wavelet com
a capacidade de aprendizado e generaliza??o das redes neurais, podendo fornecer modelos mais
exatos do que os obtidos pelas redes tradicionais. Uma evolu??o das redes WNN consiste em
combinar a estrutura neuro-fuzzyANFIS (Adaptive Network Based Fuzzy Inference System) com
estas redes, gerando-se a estrutura Fuzzy Wavelet Neural Network - FWNN. Essa rede ? muito
similar ?s redes ANFIS, com a diferen?a de que os tradicionais polin?mios presentes nos
consequentes desta rede s?o substitu?dos por redes WNN. O presente trabalho prop?e uma rede
FWNN modificada para a identifica??o de sistemas din?micos n?o lineares. Nessa estrutura,
somente fun??es waveletss?o utilizadas nos consequentes. Desta forma, ? poss?vel obter uma
simplifica??o da estrutura com rela??o a outras estruturas descritas na literatura, diminuindo o
n?mero de par?metros ajust?veis da rede. Para avaliar o desempenho da rede FWNN com essa
modifica??o, ? realizada uma an?lise das caracter?sticas da rede, verificando-se as vantagens,
desvantagens e o custo-benef?cio quando comparada com outras estruturas FWNNs. As
avalia??es s?o realizadas a partir da identifica??o de dois sistemas simulados tradicionalmente
encontrados na literatura e um sistema real n?o linear, consistindo de um tanque de multisse??es
e n?o linear. Por fim, a rede foi utilizada para inferir valores de temperatura e umidade no interior
de uma incubadora neonatal. A execu??o dessa an?lise baseia-se em v?rios crit?rios, tais como:
erro m?dio quadr?tico, n?mero de ?pocas de treinamento, n?mero de par?metros ajust?veis,
vari?ncia do erro m?dio quadr?tico, entre outros. Os resultados encontrados evidenciam a
capacidade de generaliza??o da estrutura modificada, apesar da simplifica??o realizada
| Identifer | oai:union.ndltd.org:IBICT/oai:repositorio.ufrn.br:123456789/15249 |
| Date | 24 March 2014 |
| Creators | Ara?jo J?nior, Jos? Medeiros de |
| Contributors | CPF:82675090468, http://lattes.cnpq.br/5473196176458886, Maitelli, Andr? Laurindo, CPF:42046637100, http://lattes.cnpq.br/0477027244297797, Casillo, Danielle Simone da Silva, CPF:02796900452, http://lattes.cnpq.br/2111858571672626, Almeida, Otac?lio da Mota, CPF:26310112368, http://lattes.cnpq.br/1721353262824215, Yoneyama, Takashi, CPF:73822183849, http://lattes.cnpq.br/9201712893785499, Ara?jo, F?bio Meneghetti Ugulino de |
| Publisher | Universidade Federal do Rio Grande do Norte, Programa de P?s-Gradua??o em Engenharia El?trica, UFRN, BR, Automa??o e Sistemas; Engenharia de Computa??o; Telecomunica??es |
| Source Sets | IBICT Brazilian ETDs |
| Language | Portuguese |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/doctoralThesis |
| Format | application/pdf |
| 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