Fixpointed Idempotent Uninorm (Based) Logics

Yang, Eunsuk

doi:10.3390/math7010107

Cited by 5 publications

(5 citation statements)

References 36 publications

(78 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In most study-applications of fuzzy sets or fuzzy negations, e.g., in the prediction of future events, as well as in the application of fuzzy negations in neural networks, (c.f. [9][10][11]), the classic negation N(x) = 1 − x is implicitly used. The replacement of this negation with the negations produced via conical sections offers a new approach in the application of fuzzy implications.…”

Section: Discussionmentioning

confidence: 99%

An Algorithm for Producing Fuzzy Negations via Conical Sections

Souliotis

Papadopoulos

2019

Algorithms

View full text Add to dashboard Cite

In this paper we introduced a new class of strong negations, which were generated via conical sections. This paper focuses on the fact that simple mathematical and computational processes generate new strong fuzzy negations, through purely geometrical concepts such as the ellipse and the hyperbola. Well-known negations like the classical negation, Sugeno negation, etc., were produced via the suggested conical sections. The strong negations were a structural element in the production of fuzzy implications. Thus, we have a machine for producing fuzzy implications, which can be useful in many areas, as in artificial intelligence, neural networks, etc. Strong Fuzzy Negations refers to the discrepancy between the degree of difficulty of the effort and the significance of its results. Innovative results may, therefore, derive for use in literature in the specific field of mathematics. These data are, moreover, generated in an effortless, concise, as well as self-evident manner.

show abstract

Section: Discussionmentioning

confidence: 99%

An Algorithm for Producing Fuzzy Negations via Conical Sections

Souliotis

Papadopoulos

2019

Algorithms

View full text Add to dashboard Cite

show abstract

“…When generalizing classical logic [32] in order to determine whether the fuzzy propositions are strongly true, the authors are led to evaluate the implications of fuzzy propositions [33][34][35][36].…”

Section: Theoretical Framework Of Fuzzy Implicationmentioning

confidence: 99%

“…where n(x) = 1 − x, [46][47][48], the function probor xVy = x + y − xy [32,39,41,45] has been selected for the application of t-conorm and m represents the number of probor repetitions, then…”

Section: The New Proposed Family Of Fuzzy Implicationmentioning

confidence: 99%

Generator of Fuzzy Implications

Daniilidou,

Konguetsof,

Souliotis

et al. 2023

Algorithms

View full text Add to dashboard Cite

In this research paper, a generator of fuzzy methods based on theorems and axioms of fuzzy logic is derived, analyzed and applied. The family presented generates fuzzy implications according to the value of a selected parameter. The obtained fuzzy implications should satisfy a number of axioms, and the conditions of satisfying the maximum number of axioms are denoted. New theorems are stated and proven based on the rule that the fuzzy function of fuzzy implication, which is strong, leads to fuzzy negation. In this work, the data taken were fuzzified for the application of the new formulae. The fuzzification of the data was undertaken using four kinds of membership degree functions. The new fuzzy functions were compared based on the results obtained after a number of repetitions. The new proposed methodology presents a new family of fuzzy implications, and also an algorithm is shown that produces fuzzy implications so as to be able to select the optimal method of the generator according to the value of a free parameter.

show abstract

“…At present, the development and application of logic go far beyond the field of philosophy [38][39][40]. Dynamic epistemic logic has been widely used in philosophy, sociology and computer science [41][42][43][44][45][46][47], and also in the verification and analysis of security protocols (see [7,36,37,48]).…”

Section: Related Workmentioning

confidence: 99%

Efficient Verification of Cryptographic Protocols with Dynamic Epistemic Logic

Chen

Deng

2020

Applied Sciences

View full text Add to dashboard Cite

The security of cryptographic protocols has always been an important issue. Although there are various verification schemes of protocols in the literature, efficiently and accurately verifying cryptographic protocols is still a challenging research task. In this work, we develop a formal method based on dynamic epistemic logic to analyze and describe cryptographic protocols. In particular, we adopt the action model to depict the execution process of the protocol. To verify the security, the intruder’s actions are analyzed. We model exactly the protocol applying our formal language and give the verification models according to the security requirements of this cryptographic protocol. With analysis and proof on a selected example, we show the usefulness of our method. The result indicates that the selected protocol meets the security requirements.

show abstract

Fixpointed Idempotent Uninorm (Based) Logics

Cited by 5 publications

References 36 publications

An Algorithm for Producing Fuzzy Negations via Conical Sections

An Algorithm for Producing Fuzzy Negations via Conical Sections

Generator of Fuzzy Implications

Efficient Verification of Cryptographic Protocols with Dynamic Epistemic Logic

Contact Info

Product

Resources

About