Classification of Short Circuit GMA Welding using Type-1 and Singleton Fuzzy Logic System

Campos, Rafaela Abreu; Amaral, Renan Piazzaroli Finotti; Menezes, Ivan F. M.; Goliatt, Leonardo; Lagares, Moisés Luiz; Aguiar, Eduardo Pestana de

doi:10.1109/fuzz-ieee.2018.8491599

Cited by 1 publication

(3 citation statements)

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“…The Θ in Equations (3-6) and (3-7) is the same as that used in T1-FLSMO (see Equation (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)). Additionally, it is important to define the upper vector of FBFs Φ x (q) and lower vector of FBFs Φ x (q) , as…”

Section: The Interval Type-2 Fuzzy Logic Systems Multiple Outputsmentioning

confidence: 99%

“…Taghavifar and Rakheja [19] applied the FLS together with ANN to obtain the desired pathtacking and lane-keeping of autonomous vehicles. In [20] the authors applied an FLS to classify Short Circuit GMA Welding. Moreover, Wu et al [21] defined risk factors for ship-bridge collision using an FLS approach.…”

Section: List Of Abreviations 1 Introductionmentioning

confidence: 99%

“…The results obtained in [35] indicate that both training methods lead to less computational cost and a tendency to use fewer rules when compared to evolutionary fuzzy approaches. Also, this approach has shown promising results for classification problems applied to diagnostic gas turbines [36], switch machines faults [37], and welding process [20]. On the other hand, the second challenge is related to the type-reducer, which transforms a type-2 output fuzzy set into a type-1 output fuzzy set to be used for computing the crisp output during defuzzification [5].…”

Section: List Of Abreviations 1 Introductionmentioning

confidence: 99%

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On Interval Type-2 Fuzzy Logic System Using the Upper and Lower Method for Supervised Classification Problems

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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 modeling improvement is also accompanied by an increase in mathematical and computational effort. Aiming to reduce these issues 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.

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