A review on interval type-2 fuzzy logic applications in intelligent control

Castillo, Oscar; Melín, Patricia

doi:10.1016/j.ins.2014.04.015

Cited by 255 publications

(87 citation statements)

References 128 publications

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“…4.-Applicability. Various applications have employed type-2 fuzzy sets, including the work of Mendel et al on computing with words and perceptual computing [95], [96], [98], [99], by Hagras [68], [69], by Castillo in control [35], and by Xia et al in networks ( [150]). …”

Section: Some Considerations About T2fssmentioning

confidence: 99%

A Historical Account of Types of Fuzzy Sets and Their Relationships

Bustince

Barrenechea

Pagola

et al. 2016

IEEE Trans. Fuzzy Syst.

408

159

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Section: Some Considerations About T2fssmentioning

confidence: 99%

A Historical Account of Types of Fuzzy Sets and Their Relationships

Bustince

Barrenechea

Pagola

et al. 2016

IEEE Trans. Fuzzy Syst.

408

159

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“…It is worth pointing out that we also show the relation between partial order of gradual intervals and partial order of fuzzy intervals (fuzzy truth values) used in the type-2 fuzzy sets setting (see e. g. [5,18]). …”

Section: Introductionmentioning

confidence: 60%

OWA operator for discrete gradual intervals: implications to fuzzy intervals and multi-expert decision making

Takáč¹

2016

Kybernetika

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“…These advantages allow a relatively easy implementation in specific applications, for example, [1,2] provide a review of control applications, [3] is an overview of pattern recognition and classification, [4][5][6] details hardware implementations of fuzzy systems, image processing [7], and many other applications as mentioned in [8]. However, the implementation of these algorithms usually requires a higher computational cost with respect to their classical counter parts.…”

Section: Introductionmentioning

confidence: 99%

New Methodology to Approximate Type-Reduction Based on a Continuous Root-Finding Karnik Mendel Algorithm

2017

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Interval Type-2 fuzzy systems allow the possibility of considering uncertainty in models based on fuzzy systems, and enable an increase of robustness in solutions to applications, but also increase the complexity of the fuzzy system design. Several attempts have been previously proposed to reduce the computational cost of the type-reduction stage, as this process requires a lot of computing time because it is basically a numerical approximation based on sampling, and the computational cost is proportional to the number of samples, but also the error is inversely proportional to the number of samples. Several works have focused on reducing the computational cost of type-reduction by developing strategies to reduce the number of operations. The first type-reduction method was proposed by Karnik and Mendel (KM), and then was followed by its enhanced version called EKM. Then continuous versions were called CKM and CEKM, and there were variations of this and also other types of variations that eliminate the type-reduction process reducing the computational cost to a Type-1 defuzzification, such as the Nie-Tan versions and similar enhancements. In this work we analyzed and proposed a variant of CEKM by viewing this process as solving a root-finding problem, in this way taking advantage of existing numerical methods to solve the type-reduction problem, the main objective being eliminating the type-reduction process and also providing a continuous solution of the defuzzification.

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A review on interval type-2 fuzzy logic applications in intelligent control

Cited by 255 publications

References 128 publications

A Historical Account of Types of Fuzzy Sets and Their Relationships

A Historical Account of Types of Fuzzy Sets and Their Relationships

OWA operator for discrete gradual intervals: implications to fuzzy intervals and multi-expert decision making

New Methodology to Approximate Type-Reduction Based on a Continuous Root-Finding Karnik Mendel Algorithm

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