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Previous issue date: 2016-09-26 / - / This thesis presents new fuzzy models applied to classification problems. With this regards,
we introduce the use of set-membership concept, derived from the adaptive filter
theory, into the training procedure of type-1 and singleton/non-singleton fuzzy logic systems,
in order to reduce computational complexity and to increase convergence speed.
Also, we present different criteria for using together with set-membership. Furthermore,
we discuss the usefulness of delta rule delta, local Lipschitz estimation, variable step size
and variable step size adaptive algorithms to yield additional improvement in terms of
computational complexity reduction and convergence speed. Another important contribution
of this thesis is to address the height type-reduction and to propose a modified
version of interval singleton type-2 fuzzy logic system, so−called upper and lower singleton
type-2 fuzzy logic system. The obtained results are compared with other models
reported in the literature, demonstrating the effectiveness of the proposed classifiers and
revealing that the proposals are able to properly handle with uncertainties associated with
the measurements and with the data that are used to tune the parameters of the model.
Based on data set provided by a Brazilian railway company, the models outlined above are
applied in the classification of three possible faults and the normal condition of the switch
machine, which is an equipment used for handling railroad switches. Finally, this thesis
discusses the use of set-membership concept into the training procedure of an interval
and singleton type-2 fuzzy logic system and of an upper and lower singleton type-2 fuzzy
logic system, aiming to reduce computational complexity and to increase the convergence
speed and the classification ratio. Also, we discuss the adoption of different criteria together
with set-membership based-techniques. The performance is based on the data set
composed of images provided by the same Brazilian railway company, which covers the
four possible rail head defects and the normal condition of the rail head. The reported results
show that the proposed models result in improved convergence speed, slightly higher
classification ratio and remarkable computation complexity reduction when we limit the
number of epochs for training, which may be required due to real time constraint or low
computational resource availability.
| Identifer | oai:union.ndltd.org:IBICT/oai:hermes.cpd.ufjf.br:ufjf/3627 |
| Date | 26 September 2016 |
| Creators | Aguiar, Eduardo Pestana de |
| Contributors | Ribeiro, Moisés Vidal, Vellasco, Marley Maria Bernardes Rebuzzi, Amaral, Jorge Luís Machado do, Caminhas, Walmir Matos, Marcato, André Luís Marques, Oliveira, Leonardo Willer de |
| Publisher | Universidade Federal de Juiz de Fora (UFJF), Programa de Pós-graduação em Engenharia Elétrica, UFJF, Brasil, Faculdade de Engenharia |
| Source Sets | IBICT Brazilian ETDs |
| Language | Portuguese |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/doctoralThesis |
| Source | reponame:Repositório Institucional da UFJF, instname:Universidade Federal de Juiz de Fora, instacron:UFJF |
| Rights | info:eu-repo/semantics/openAccess |
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