On the choice of similarity measures for type-2 fuzzy sets

McCulloch, Josie; Wagner, Christian

doi:10.1016/j.ins.2019.09.027

Cited by 23 publications

(7 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…IAA method can effectively reduce the quantity and degree of assumptions. In addition, this method constructs nonparametric model based on interval data without determining the specific type of FSs (such as Gaussian and Triangular) 50 , 51 . At same times it can greatly diminish the loss of information when reduce the higher ordered model 52 .…”

Section: Methodsmentioning

confidence: 99%

The assessment of smart city information security risk in China based on zGT2FSs and IAA method

Zhao

Wang

Liu

2022

Sci Rep

View full text Add to dashboard Cite

The continuous expansion of the construction scale of smart city has reconstructed the urban information pattern. How to maintain the stability of information security while giving full play to the role of information sharing is a practical problem that must be solved for the sustainable development of smart city. Based on the information ecology theory, this paper construct the smart city information security risk evaluation system from six aspects. Then, zGT2FSs is established based on type-2 fuzzy set theory and IAA method, which fully considers the internal and external uncertainty of expert decision-making. According to the calculation results, the key influencing factors of information security risk of smart city are analyzed to provide suggestions and guidance for the formulation of information security control in the process of smart city construction in China.

show abstract

Section: Methodsmentioning

confidence: 99%

The assessment of smart city information security risk in China based on zGT2FSs and IAA method

Zhao

Wang

Liu

2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…[15] and Section 3, based mostly on traditional and on interval-valued fuzzy sets (aka interval type-2 fuzzy sets), are the main premise to consider general type-2 fuzzy sets and type-2 fuzzy logic systems to be applied. This idea is mainly inspired by many successful applications of type-2 fuzzy logic systems [27,28,29,30,31,32] supported via pioneer descriptions and explanations of building T2FLSs by Mendel et al [22,33,34,35,36]. Our first attempt to apply higher order fuzzy logic systems in managing the SCR process is presented in [15], however from the current point of view, it was limited to interval type-2 fuzzy set only; here, we present T2FLSs based on general (mostly triangular type-2 fuzzy sets) and the results achieved are promising.…”

Section: Type-2 Fuzzy Logic Systems Managing Data In the Scr Processmentioning

confidence: 99%

Type-2 Fuzzy Logic Systems in Applications: Managing Data in Selective Catalytic Reduction for Air Pollution Prevention

Niewiadomski

Kacprowicz

2021

Journal of Artificial Intelligence and Soft Computing Research

View full text Add to dashboard Cite

The article presents our research on applications of fuzzy logic to reduce air pollution by DeNOx filters. The research aim is to manage data on Selective Catalytic Reduction (SCR) process responsible for reducing the emission of nitrogen oxide (NO) and nitrogen dioxide (NO2). Dedicated traditional Fuzzy Logic Systems (FLS) and Type-2 Fuzzy Logic Systems (T2FLS) are proposed with the use of new methods for learning fuzzy rules and with new types of fuzzy implications (the so-called ”engineering implications”). The obtained results are consistent with the results provided by experts. The main advantage of this paper is that type-2 fuzzy logic systems with ”engineering implications” and new methods of learning fuzzy rules give results closer to expert expectations than those based on traditional fuzzy logic systems. According to the literature review, no T2FLS were applied to manage DeNOx filter prior to the research presented here.

show abstract

“…Firstly, the fuzzy sets [21][22] A1, A2 and A3 are taken to represent three levels of user tag weights, namely "small, moderate and large" respectively, and the corresponding ones are generated MDF [23], as shown in Figure 4 (taking social tag as an example). In this paper, gaussian function [24] is used to represent fuzzy sets.…”

Section: Determination Of Membership Degree Function (Mdf)mentioning

confidence: 99%

User tag weight assessment based on fuzzy theory in mobile social networks

Gao

Wang

et al. 2021

Preprint

View full text Add to dashboard Cite

Mobile social network supports mobile communication and asynchronous social networking. For enterprises, how to provide better services and create greater business value through the data and information provided by users is crucial. For example, enterprises need to build user profiles to achieve personalized recommendation and precision marketing. In view of the data modeling stage of user profile, we propose a method to evaluate user tag weight, which includes two steps. Specifically, we introduce fuzzy theory to get the initial weight interval. Then, genetic algorithm with single point crossover is used to optimize user tag weight. Experiment results show that the proposed method has better performance than other three methods applied to recommendation system.

show abstract

On the choice of similarity measures for type-2 fuzzy sets

Cited by 23 publications

References 48 publications

The assessment of smart city information security risk in China based on zGT2FSs and IAA method

The assessment of smart city information security risk in China based on zGT2FSs and IAA method

Type-2 Fuzzy Logic Systems in Applications: Managing Data in Selective Catalytic Reduction for Air Pollution Prevention

User tag weight assessment based on fuzzy theory in mobile social networks

Contact Info

Product

Resources

About