Tese: On Interval Type-2 Fuzzy Logic System using the Upper and Lower Method for Supervised Classification Problems
Aluno(a) : Renan Piazzaroli Finotti AmaralOrientador(a): Ivan Menezes
Área de Concentração: Termociências
Data: 03/09/2021
Link para tese/dissertação: https://doi.org/10.17771/PUCRio.acad.55169
Resumo: Fuzzy logic systems are machine learning techniques that can model mathematically uncertainties. They are divided into type-1 fuzzy, and type-2 fuzzy logic systems. The type-1 fuzzy logic system has been widely applied to solve several problems related to machine learning, such as control, classification, clustering, prediction, among others. However, as it presents a better mathematical modeling of uncertainties, the type-2 fuzzy logic system has received much attention over the years. This improvement in modeling is also accompanied by an increase in mathematical and computational complexity. Aiming to reduce these complexities to solve classification problems, this work presents the development and comparison of two Gaussian membership functions for a type-2 interval fuzzy logic system using the upper and lower method. Gaussian membership functions with uncertainty in the mean and with uncertainty in the standard deviation are used. Both fuzzy models covered in this work are trained by algorithms based on first order information. Furthermore, this work proposes the extension of interval type-2 fuzzy models to present multiple outputs, significantly reducing the computational cost in solving multiclass classification problems. Finally, aiming to contextualize the use of these models in mechanical engineering applications, this work presents the solution of a problem of fault detection in aircraft gas turbines.