Weighted fuzzy logic and its applications

He, Xiaoquan

doi:10.1109/cmpsac.1988.17224

Cited by 11 publications

(2 citation statements)

References 7 publications

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“…In future releases, we plan to extend Simpful with support for additional fuzzy logic operators and other fuzzy inference methods (e.g., Tsukamoto [50], and AnYa [51] methods). In particular, we will add support for weighted fuzzy rules [52], type-2 FISs [53], and probabilistic fuzzy reasoning [54], the latter providing a means to combine the interpretability of FIS with the statistical properties of probabilistic systems. Finally, Simpful will support the FML format defined in the IEEE Std 1855-2016 [35], possibly by leveraging existing software (i.e., JFML and Py4JFML [17,24]), to facilitate the import, export, and sharing of the FISs defined within this library.…”

Section: Discussionmentioning

confidence: 99%

Simpful: A User-Friendly Python Library for Fuzzy Logic

Spolaor¹,

Fuchs²,

Cazzaniga³

et al. 2020

IJCIS

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Many researchers have used fuzzy set theory and fuzzy logic in a variety of applications related to computer science and engineering, given the capability of fuzzy inference systems to deal with uncertainty, represent vague concepts, and connect human language to numerical data. In this work we propose Simpful, a general-purpose and user-friendly Python library designed to facilitate the definition, analysis, and interpretation of fuzzy inference systems. Simpful provides a lightweight Application Programming Interface that allows to intuitively define fuzzy sets and fuzzy rules, and to perform fuzzy inference. Worthy of note, in Simpful the fuzzy rules are specified by means of strings of text written in natural language. We provide here some practical examples to show that Simpful represents a valuable addition to the open-source software that supports fuzzy reasoning.

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Section: Discussionmentioning

confidence: 99%

Simpful: A User-Friendly Python Library for Fuzzy Logic

Spolaor¹,

Fuchs²,

Cazzaniga³

et al. 2020

IJCIS

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show abstract

“…In [32], an idea of weighted fuzzy inference was discussed. Here we will use such weighted fuzzy inferences to deal with our problem.…”

Section: Definitionmentioning

confidence: 99%

The normal numbers of the fuzzy systems and their classes

Yuan

Wang

et al. 2010

Sci. China Inf. Sci.

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R-fuzzy set is defined in this paper, which is regarded as the generalization of the Zadeh fuzzy set. By means of CRI method, some fuzzy systems are constructed by suitably using several kinds of R-fuzzy sets as fuzzy inference antecedents, such as interpolation fuzzy system, Bernstein fuzzy system, Lagrange fuzzy system and Hermite fuzzy system. A notion of the normal number of the fuzzy system is defined here, we have shown that all fuzzy systems are able to be classified as three classes such as the normal fuzzy systems, the regular fuzzy systems and the singular fuzzy systems under the significance of the normal numbers of fuzzy systems. Finally, the generalized Bernstein polynomial is obtained by constructing Bernstein fuzzy system, it is proved that the generalized Bernstein polynomial is uniformly convergent in C[a, b] under a weaker condition, and it is pointed out that there exist generalized Bernstein polynomials to be not convergent in C [a, b] by use of constructing a counterexample.KeywordsR-fuzzy set, fuzzy system, the normal number of fuzzy system, universal approximation property for fuzzy system CitationLi H X, Yuan X H, Wang J Y, et al. The normal numbers of the fuzzy systems and their classes. Sci

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New Methods for Evaluating the Answerscripts of Students Using Fuzzy Sets

Hui-yu

Chen

2006

Advances in Applied Artificial Intelligence

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Weighted fuzzy logic and its applications

Cited by 11 publications

References 7 publications

Simpful: A User-Friendly Python Library for Fuzzy Logic

Simpful: A User-Friendly Python Library for Fuzzy Logic

The normal numbers of the fuzzy systems and their classes

New Methods for Evaluating the Answerscripts of Students Using Fuzzy Sets

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