Simplified interval type-2 fuzzy logic system based on new type-reduction

El-Nagar, Ahmad M.; El-Bardini, Mohammad

doi:10.3233/ifs-141166

Cited by 34 publications

(14 citation statements)

References 38 publications

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“…In this section, we provide two kinds of noniterative for performing the centroid TR of IT2 FLSs; they are the NB and NT algorithms [24,29,30,43].…”

Section: Nb and Nt Algorithmsmentioning

confidence: 99%

“…NB Algorithms. The NB algorithms [24] offered a closed form of TR. After the process of fuzzy reasoning [16], letb e the obtained output IT2 FS and the primary variable be equally discretized into points that satisfies 1 < 2 < ⋅ ⋅ ⋅ < ; then the two endpoints of centroid interval can be computed as…”

Section: Nb and Nt Algorithmsmentioning

confidence: 99%

“…Mendel and Liu analyzed and proved the monotoncity and superexponential convergence [23] of CKM algorithms. In recent years, Nagar and Bardini verified that IT2 FLSs based on the NB algorithms [24] have superior performances to respond to the effect of uncertainties in the systems' parameters to the IT2 FLSs based on other typereduction (TR) algorithms like EKM, Wu-Mendel Uncertainty Bound (WM-UB [25]), Begian Melek Mendel (BMM [26,27]), and Greenfield-Chiclana Collapsing Defuzzifier (GCCD [28]). Furthermore, Nie-Tan (NT) algorithms [29] were proved to be excellent approach to simplify IT2 FLSs.…”

Section: Introductionmentioning

confidence: 99%

“…While the continuous versions of NT (CNT) algorithms [30] were verified to be accurate algorithms for solving the centroids of IT2 FSs, in a word, all these works laid enrich theoretical foundations for studying and applying the TR algorithms of IT2 FLSs. Inspired by [16,19,[22][23][24][29][30][31][32][33][34][35][36][37], this paper proposes the continuous of NB and NT (CNB and CNT) algorithms and brings connections between NB, NT, and BMM algorithms. The blocks of fuzzy reasoning, centroid type-reduction, and defuzzification of IT2 FLSs are performed by combining the NB and NT algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Study on Centroid Type-Reduction of Interval Type-2 Fuzzy Logic Systems Based on Noniterative Algorithms

Chen

2019

Complexity

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Interval type-2 fuzzy logic systems have favorable abilities to cope with uncertainties in many applications. While the block type-reduction under the guidance of inference plays the central role in the systems, Karnik-Mendel (KM) iterative algorithms are standard algorithms to perform the type-reduction; however, the high computational cost of type-reduction process may hinder them from real applications. The comparison between the KM algorithms and other alternative algorithms is still an open problem. This paper introduces the related theory of interval type-2 fuzzy sets and discusses the blocks of fuzzy reasoning, type-reduction, and defuzzification of interval type-2 fuzzy logic systems by combining the Nagar-Bardini (NB) and Nie-Tan (NT) noniterative algorithms for solving the centroids of output interval type-2 fuzzy sets. Moreover, the continuous version of NT (CNT) algorithms is proved to be accurate algorithms for performing the type-reduction. Four computer simulation examples are provided to illustrate and analyze the performances of two kinds of noniterative algorithms. The NB and NT algorithms are superior to the KM algorithms on both calculation accuracy and time, which afford the potential application value for designer and adopters of type-2 fuzzy logic systems.

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“…In this section, we provide two kinds of noniterative for performing the centroid TR of IT2 FLSs; they are the NB and NT algorithms [24,29,30,43].…”

Section: Nb and Nt Algorithmsmentioning

confidence: 99%

Section: Nb and Nt Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Study on Centroid Type-Reduction of Interval Type-2 Fuzzy Logic Systems Based on Noniterative Algorithms

Chen

2019

Complexity

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“…However, these linear models restrict their applications on nonlinear and seasonal problems greatly influenced by uncertainty. Developing nonlinear methods like neural networks and fuzzy logic systems (FLSs) on the forecasting problems has attracted broad attentions. A recent study shows that interval type‐2 FLSs (IT2 FLSs) own more superior approximation abilities than the nonparametric methods like neural networks for forecasting problems.…”

Section: Introductionmentioning

confidence: 99%

Forecasting by TSK general type‐2 fuzzy logic systems optimized with genetic algorithms

Chen

Wang

2017

Optim Control Appl Methods

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SummaryResearching the theory and applications of general type-2 fuzzy logic systems (GT2 FLSs) has become a hot orientation in recent years. The permanent-magnetic drive (PMD) affected by uncertainties is an emerging technology. This paper designs a type of Takagi-Sugeno-Kang GT2 FLSs to investigate PMD temperature forecasting problems. Genetic algorithms are used to optimize the parameters of Takagi-Sugeno-Kang GT2 FLSs, according to the asymptotic way. The primary membership functions (MFs) of the antecedent and input measurements of the proposed systems are chosen as the Gaussian-type MFs with uncertain standard deviations, and the consequent parameters are selected as crisp numbers, whereas the secondary MFs (vertical slices) are selected as a triangle type. Noisy data of PMD temperature are used for training and testing the proposed T2 FLS forecasting methods. Numerical simulation studies and convergence analysis illustrate that the proposed GT2 FLSs outperform their type-1 and interval type-2 FLSs.

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Interval Type-2 Fuzzy Systems

Mendel

2024

Explainable Uncertain Rule-Based Fuzzy Systems

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Simplified interval type-2 fuzzy logic system based on new type-reduction

Cited by 34 publications

References 38 publications

Study on Centroid Type-Reduction of Interval Type-2 Fuzzy Logic Systems Based on Noniterative Algorithms

Study on Centroid Type-Reduction of Interval Type-2 Fuzzy Logic Systems Based on Noniterative Algorithms

Forecasting by TSK general type‐2 fuzzy logic systems optimized with genetic algorithms

Interval Type-2 Fuzzy Systems

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