Fuzzy Sets of Type-2

1999 ◽  
Vol 3 (6) ◽  
pp. 499-508 ◽  
Author(s):  
Robert John ◽  
Keyword(s):  
Knowledge Representation ◽  
Fuzzy Sets ◽  
Fuzzy Systems ◽  
Medical Application ◽  
The Other ◽  
Detailed Review

This paper provides a detailed review of the important and growing role that fuzzy sets of type-2 play in knowledge representation and inferencing with fuzzy systems. As well as an up-to-date review of the work in this area, examples are provided that demonstrate how type-2 sets can help with both knowledge representation and inferencing. The paper also reports on the use of type-2 sets in a medical application and summarizes the other type-2 applications reported in the literature.

Overview of Type-2 Fuzzy Logic Systems

2012 ◽  
Vol 2 (4) ◽  
pp. 1-28 ◽  
Author(s):  
Ahmad Taher Azar
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Fuzzy Systems ◽  
Degrees Of Freedom ◽  
Fuzzy Logic Systems ◽  
Logic Systems

Fuzzy set theory has been proposed as a means for modeling the vagueness in complex systems. Fuzzy systems usually employ type-1 fuzzy sets, representing uncertainty by numbers in the range [0, 1]. Despite commercial success of fuzzy logic, a type-1 fuzzy set (T1FS) does not capture uncertainty in its manifestations when it arises from vagueness in the shape of the membership function. Such uncertainties need to be depicted by fuzzy sets that have blur boundaries. The imprecise boundaries of a type-2 fuzzy set (T2FS) give rise to truth/membership values that are fuzzy sets in [0], [1], instead of a crisp number. Type-2 fuzzy logic systems (T2FLSs) offer opportunity to model levels of uncertainty which traditional fuzzy logic type1 struggles. This extra dimension gives more degrees of freedom for better representation of uncertainty compared to type-1 fuzzy sets. A type-1 fuzzy logic system (T1FLSs) inference produces a T1FS and the result of defuzzification of the T1FS, a crisp number, whereas a T2FLS inference produces a type-2 fuzzy set, its type-reduced fuzzy set which is a T1FS and the defuzzification of the type-1 fuzzy set. The type-reduced fuzzy set output gives decision-making flexibilities. Thus, FLSs using T2FS provide the capability of handling a higher level of uncertainty and provide a number of missing components that have held back successful deployment of fuzzy systems in decision making.

Type 2 Fuzzy Sets: An Appraisal of Theory and Applications

1998 ◽  
Vol 06 (06) ◽  
pp. 563-576 ◽  
Author(s):  
Robert John
Keyword(s):  
Knowledge Representation ◽  
Fuzzy Sets ◽  
Worked Examples

This paper provides a guide and tutorial to type 2 fuzzy sets. Type 2 fuzzy sets allow for linguistic grades of membership thus assisting in knowledge representation. They also offer improvement on inferencing with type 1 sets. The various approaches to knowledge representation and inferencing are discussed, with worked examples, and some of the applications of type 2 sets are reported.

scholarly journals Mediative Fuzzy Extension Technique and Its Consistent Measurement in the Decision Making of Medical Application

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
M. K. Sharma ◽  
Nitesh Dhiman ◽  
Lakshmi Narayan Mishra ◽  
Vishnu Narayan Mishra ◽  
Suresh Kumar Sahani
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Expert Knowledge ◽  
Medical Application ◽  
Extension Principle ◽  
Medical Field ◽  
Research Article ◽  
Fuzzy Ranking ◽  
Ranking Technique

The notion of fuzzy set theory has not so far been districted over medical diagnosis. There are some added applications, for example, in image processing, pattern identification, and many medical devices. In this research article, we introduced a new mediative fuzzy ranking technique as the fuzzy extension in decision making. The proposed mediative fuzzy logic-based technique is more relevant and applicable to incomplete and doubtful situations or some contradictions present in the expert knowledge. The value of the contradictory degree for mediative fuzzy sets used in the extension principle is defined. The proposed mediative fuzzy ranking method is easily implemented in the medical field, and the proposed mediative fuzzy extension-based measured technique is useful to medical experts and doctors in many decision-making situations; the entire work is illustrated with numerical examples. We have also given some future aspects of mediative fuzzy extension in the interpretation of type-2 intuitionistic fuzzy sets.

MAIN SPECIFICATIONS OF ASSESSING ACTIVITIES OF MINOR IMPORTANCE OF IN MODERN CRIMINAL LAW

Law and World ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 83-95
Keyword(s):  
Criminal Law ◽  
Criminal Liability ◽  
Case Law ◽  
Criminal Code ◽  
The Other ◽  
European Countries ◽  
Minor Importance ◽  
Detailed Review

The research includes the full and the detailed overview of assessing activities of minor importance in Georgian Criminal Law. The Article 7 of the Criminal Code of Georgia states the following: a crime shall not be an action that, although formally containing the signs of a crime, has not produced, for minor importance, the prejudice that would require criminal liability of its perpetrator, or has not created the risk of such harm. The research includes the main criteria of defining activities as activities of minor importance. The detailed review of Georgian case law is also introduced, as well as, legislation, judicial literature and experience of the other European countries.

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