Extending fuzzy logics with many hedges

Le, Van Hung; Tran, Duc Khanh

doi:10.1016/j.fss.2018.01.014

Cited by 14 publications

(10 citation statements)

References 25 publications

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“…The interval arithmetic presented by Moore (1966) is used together with the extension principle on several membership α-cuts of the fuzzy number x j for evaluating fuzzy expressions (Buckley and Qu, 1990;Buckley and Hayashi, 1999;Buckley and Feuring, 2000). The fuzzy sets can be modified by intensifying or weakening modifiers (De Cock and Kerre, 2004;Le and Tran, 2018) Type-2 fuzzy models introduced by Zadeh (1975) take into account uncertainty about the membership function (Mendel, 2007;Sadeghian et al, 2013).…”

Section: Knowledge-based Methodologiesmentioning

confidence: 99%

Intelligent Multimodel Simulation in Decomposed Systems

Juuso¹

2018

Linköping Electronic Conference Proceedings

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Intelligent methodologies provide a good basis for multimodel simulation. Small, specialised systems have a large number of feasible solutions, but developing truly adaptive, and still understandable, systems for highly complex systems require domain expertise and more compact approaches at the basic level. The nonlinear scaling approach extends the application areas of linear methodologies to nonlinear modelling and reduces the need for decomposition with local models. Fuzzy set systems provide a good basis for understandable models for decomposed systems. Data-based methodologies are suitable for developing these adaptive applications via the following steps: variable analysis, linear models and intelligent extensions. Complex problems are solved level by level to keep the domain expertise as an essential part of the solution.

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Section: Knowledge-based Methodologiesmentioning

confidence: 99%

Intelligent Multimodel Simulation in Decomposed Systems

Juuso¹

2018

Linköping Electronic Conference Proceedings

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“…Novak et al have proposed perception-based logic deduction to describe and process evaluative linguistic expressions [19]- [22]. By using linguistic hedges, Ho et al have presented hedge algebra to represent and reason linguistic human knowledge with particular truth values of vague sentences [23]- [27]. Pei et al provided linguistic formal concept lattice to analyze the relation and hierarchical structure of linguistic values [12];…”

Section: B Via Fuzzy Logic or Algebra On The Set Of Linguistic Valuesmentioning

confidence: 99%

“…Multi-granular linguistic terms are to satisfy the need of multi-experts or multiple sources of linguistic information in practical applications, which are managed by the hierarchy of multi-granular linguistic terms, such as a linguistic hierarchy [38] or linguistic hierarchical tree [48] have be constructed to manage multi-granular linguistic terms. In [12] [23]- [27], h 0 is also called as identity, i.e., < h 0 ><primary linguistic term>=primary linguistic term. According to effects of hedges on fuzziness, a linear order on linguistic hedges can be defined as follows:…”

Section: A the Hierarchy Of Generated Linguistic Termsmentioning

confidence: 99%

Constructing and Managing Multi-Granular Linguistic Values Based on Linguistic Terms and Their Fuzzy Sets

Pei

Ren

2019

IEEE Access

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Constructing and managing multi-granular linguistic values are more and more important for linguistic decision making in big data or social computing environments, linguistic variable is the fundamental of constructing and managing multi-granular linguistic values. Based on analysis of linguistic values and drawbacks of symbolic or fuzzy set methods in processing linguistic information, a linguistic value is expressed by a formal linguistic concept, which is constructed by a linguistic term and it's fuzzy sets, i.e., intension (name) and extension (meaning) of the concept are a linguistic term and it's fuzzy sets. A new symbolic translation based on fuzzy sets is provided to obtain formal 2-tuple linguistic concepts, which are continuous formal linguistic concepts. By using linguistic hedges, the hierarchy of multi-granular formal linguistic concepts is constructed, and managing multi-granular linguistic values is carried out by a new transformation function between formal linguistic concepts of the hierarchy. Cases study shows that the proposed method combines advantages of symbolic approaches and fuzzy set methods in linguistic information processing and overcomes their drawbacks due to fuzzy sets and linguistic term as entity in linguistic information processing based on formal linguistic concepts, intensions are utilized to deal with linguistic information and extensions are used to represent meanings and obtain natural or artificial language concepts. It seems that constructing and managing multi-granular linguistic values via formal linguistic concepts is an useful and alternative method in linguistic information processing.

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“…The proposed axiomatizations are expanded to first-order level. Generally, this paper proposes an approach in the construction of linguistic or descriptive fuzzy logic expressions depending on the axiomatization and algebra for hedges, wherein the truth values of ambiguous phrases are displayed in linguistic expressions [9].…”

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy Logic Control System with Gaussian Membership Functions

Africa¹,

Arevalo²,

Publico³

et al. 2019

IJETER

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One of the most widely known advanced control techniques is Fuzzy Logic Control. This paper aims to define the concept and function of Fuzzy Logic Control. Furthermore, this paper consists of brief discussion on how this type of control works, and how it is applied to several types of control systems. The procedure followed by this approach is explained and presented in this paper as well. This includes a discussion on the three steps namely fuzzification, Fuzzy Logic has become a popular approach in researches in the fields of science, technology, engineering, and medicine. This paper also shows the versatility of this approach in terms of numerous functions and applications.

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Extending fuzzy logics with many hedges

Cited by 14 publications

References 25 publications

Intelligent Multimodel Simulation in Decomposed Systems

Intelligent Multimodel Simulation in Decomposed Systems

Constructing and Managing Multi-Granular Linguistic Values Based on Linguistic Terms and Their Fuzzy Sets

Fuzzy Logic Control System with Gaussian Membership Functions

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