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Enhanced Interval Approach for Encoding Words Into Interval Type-2 Fuzzy Sets and Its Convergence Analysis

This paper proposes an enhanced interval approach (EIA) and demonstrates its performance on data that are collected from a web survey and shows that the EIA converges in a mean-square sense and generally, 30 data intervals seem to be a good compromise between cost and accuracy.
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Geometric Type-1 and Type-2 Fuzzy Logic Systems

This paper provides new algorithms for various operations on type-1 and type-2 fuzzy sets and for defuzzification and indicates that this approach reduces the execution speed of these operations.
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The collapsing method of defuzzification for discretised interval type-2 fuzzy sets

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Type-2 Fuzzy Logic: A Historical View

  • R. JohnS. Coupland
  • Computer Science
    IEEE Computational Intelligence Magazine
  • 1 February 2007
A historical perspective of the development of type-2 fuzzy logic is provided with a retrospective review of important developments in the field to highlight areas where the technology will have a significant role to play in the future.
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A Fast Geometric Method for Defuzzification of Type-2 Fuzzy Sets

A new method for assessing the accuracy of the membership function of a type-2 fuzzy set and the differences between the new approach and type-reduction are discussed, identifying the origin of this massive improvement in execution speed.
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On Nie-Tan Operator and Type-Reduction of Interval Type-2 Fuzzy Sets

It is proved that the closed-form Nie-Tan operator, which outputs the average of the upper and lower bounds of the footprint of uncertainty, is actually an accurate method for defuzzifying interval type-2 fuzzy sets.
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Enhanced Interval Approach for encoding words into interval type-2 fuzzy sets and convergence of the word FOUs

The paper shows by means of some simulations, that the IT2 FS word models that are obtained from the EIA are converging in a mean-square sense, which provides substantial credence for using the E IA to obtain T2FS word models.
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A fast and efficient semantic short text similarity metric

A novel Short Text Semantic Similarity (STSS) method is presented to address the issues that arise with sparse text representation, and is shown to be comparable to current semantic similarity approaches, LSA and STASIS, whilst having a lower computational footprint.
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Interval type-2 fuzzy decision making

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Fuzzy Transfer Learning: Methodology and application

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