On Computing Normalized Interval Type-2 Fuzzy Sets

Mendel, Jerry M.; Rajati, Mohammad Reza

doi:10.1109/tfuzz.2013.2280133

Cited by 18 publications

(7 citation statements)

References 26 publications

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“…Definition 3 (Chaturvedi, Cambria, Welsch, & Herrera, 2017;Chen & Lee, 2010;Mendel & Rajati, 2014): When all µ̃ (x, u)=1, then Ã is called an interval type-2 fuzzy set. An interval type-2 fuzzy set Ã can be expressed as a special case of a general type-2 fuzzy set, as:…”

Section: Definitionsmentioning

confidence: 99%

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Computing with words for student peer assessment in oral presentation

rahmanian

Shafieian

Samie

2021

Nexo Revista Científica

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Peer assessment in an oral presentation can motivate and give more sense of responsibility to students. In recent years, various methods have been proposed to evaluate peers. In this paper, a novel peer online assessment method is proposed for oral presentation using perceptual computing. The output of the proposed system can be a numerical score for the overall assessment of a student in the presentation, which allows comparison and ranking of student performance. Furthermore, a linguistic evaluation that describes the student's performance is obtained from the system. A case study has been conducted to show the effectiveness of the proposed method; then the results are analyzed and reviewed.

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Section: Definitionsmentioning

confidence: 99%

“…Definition 4 (Chaturvedi et al, 2017;Chen & Lee, 2010;Mendel & Rajati, 2014): An IT2FS, Ã , is described by its FOU, i.e., FOU (Ã) , where FOU (Ã) is described by its LMF and UMF of Ã, i.e., ̃, ̃ respectively, as shown in Figure 1. Both ̃ and ̃ are T1FSs, as follows:…”

Section: Definitionsmentioning

confidence: 99%

Computing with words for student peer assessment in oral presentation

rahmanian

Shafieian

Samie

2021

Nexo Revista Científica

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“…According to [9][10][11][12] fuzzy logic type 2 has advantages over fuzzy logic type 1 in handling uncertainties in memberships functions and in unexpected disturbances and is very much used in traffic [13], for power control [14], fault detection [15] and image processing [16] and control for Dual Star Induction Machine [17].…”

Section: Introductionmentioning

confidence: 99%

Sliding Control with Fuzzy Type-2 Controller of Wind Energy System Based on Doubly Fed Induction Generator

Zouggar¹,

Chaouch²,

Abdeslam³

et al. 2019

I2M

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The wind system based on double-fed induction generator (DFIG) has become a very important source of energy. To ensure the proper functioning of this system, many improved control algorithms have been developed for the rotor side converter (RSC). This article presents the analysis and design of a double-fed induction generator (DFIG) control technique based on coupling the type-2 fuzzy logic control with the sliding mode control (SMC). For the design of this technique, a decoupled modeling of the DFIG with the orientation of its stator flow is presented. The main purpose of the proposed technique is to make a control to meter the quantities of the powers produced by DFIG which are injected into the electrical network and to reduce the phenomenon of chattering which depends on control by sliding mode. This command has allowed us to reduce the chattering phenomenon and improve the performance of the system in terms of speed monitoring and stator side powers regulation. Simulations are performed using MATLAB / Simulink to validate the effectiveness of the proposed control algorithm. The simulation results, obtained when applying this control strategy to the system, demonstrated the validity of the results and thus validated the high performance of this control technique.

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“…With its fast convergence, it is still the most widely used algorithm for computing the switch points [2,3,4,5,6]. It was originally shown that the maximum number of iterations of the KM algorithm is N , which is the number of discrete points in the universe of discourse for an IT2 fuzzy set.…”

Section: Introductionmentioning

confidence: 99%

A Direct Approach for Determining the Switch Points in the Karnik–Mendel Algorithm

Chen

John

Twycross

et al. 2018

IEEE Trans. Fuzzy Syst.

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A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. Abstract-The Karnik-Mendel algorithm is used to compute the centroid of interval type-2 fuzzy sets, determining the switch points needed for the lower and upper bounds of the centroid, through an iterative process. It is commonly acknowledged that there is no closed-form solution for determining such switch points. Many enhanced algorithms have been proposed to improve the computational efficiency of the Karnik-Mendel algorithm. However, all of these algorithms are still based on iterative procedures. In this paper, a direct approach based on derivatives for determining the switch points without multiple iterations has been proposed, together with mathematical proof that these switch points are correctly determining the lower and upper bounds of the centroid. Experimental simulations show that the direct approach obtains the same switch points, but is more computationally efficient than any of the existing (iterative) algorithms. Thus, we propose that this algorithm should be used in any application of interval type-2 fuzzy sets in which the centroid is required.

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On Computing Normalized Interval Type-2 Fuzzy Sets

Cited by 18 publications

References 26 publications

Computing with words for student peer assessment in oral presentation

Computing with words for student peer assessment in oral presentation

Sliding Control with Fuzzy Type-2 Controller of Wind Energy System Based on Doubly Fed Induction Generator

A Direct Approach for Determining the Switch Points in the Karnik–Mendel Algorithm

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