177 research outputs found
Strict Intuitionistic Fuzzy Distance/Similarity Measures Based on Jensen-Shannon Divergence
Being a pair of dual concepts, the normalized distance and similarity
measures are very important tools for decision-making and pattern recognition
under intuitionistic fuzzy sets framework. To be more effective for
decision-making and pattern recognition applications, a good normalized
distance measure should ensure that its dual similarity measure satisfies the
axiomatic definition. In this paper, we first construct some examples to
illustrate that the dual similarity measures of two nonlinear distance measures
introduced in [A distance measure for intuitionistic fuzzy sets and its
application to pattern classification problems, \emph{IEEE Trans. Syst., Man,
Cybern., Syst.}, vol.~51, no.~6, pp. 3980--3992, 2021] and [Intuitionistic
fuzzy sets: spherical representation and distances, \emph{Int. J. Intell.
Syst.}, vol.~24, no.~4, pp. 399--420, 2009] do not meet the axiomatic
definition of intuitionistic fuzzy similarity measure. We show that (1) they
cannot effectively distinguish some intuitionistic fuzzy values (IFVs) with
obvious size relationship; (2) except for the endpoints, there exist infinitely
many pairs of IFVs, where the maximum distance 1 can be achieved under these
two distances; leading to counter-intuitive results. To overcome these
drawbacks, we introduce the concepts of strict intuitionistic fuzzy distance
measure (SIFDisM) and strict intuitionistic fuzzy similarity measure (SIFSimM),
and propose an improved intuitionistic fuzzy distance measure based on
Jensen-Shannon divergence. We prove that (1) it is a SIFDisM; (2) its dual
similarity measure is a SIFSimM; (3) its induced entropy is an intuitionistic
fuzzy entropy. Comparative analysis and numerical examples demonstrate that our
proposed distance measure is completely superior to the existing ones
NEW DISSIMILARITY MEASURES ON PICTURE FUZZY SETS AND APPLICATIONS
The dissimilarity measures between fuzzy sets/intuitionistic fuzzy sets/picture fuzzy sets are studied and applied in various matters. In this paper, we propose some new dissimilarity measures on picture fuzzy sets. This new dissimilarity measures overcome the restrictions of all existing dissimilarity measures on picture fuzzy sets. After that, we apply these new measures to the pattern recognition problems. Finally, we introduce a multi-criteria decision making (MCDM) method that used the new dissimilarity measures and apply them in the supplier selection problems
A New Similarity Measure between Intuitionistic Fuzzy Sets and Its Application to Pattern Recognition
As a generation of ordinary fuzzy set, the concept of intuitionistic fuzzy set (IFS), characterized both by a membership degree and by a nonmembership degree, is a more flexible way to cope with the uncertainty. Similarity measures of intuitionistic fuzzy sets are used to indicate the similarity degree between intuitionistic fuzzy sets. Although many similarity measures for intuitionistic fuzzy sets have been proposed in previous studies, some of those cannot satisfy the axioms of similarity or provide counterintuitive cases. In this paper, a new similarity measure and weighted similarity measure between IFSs are proposed. It proves that the proposed similarity measures satisfy the properties of the axiomatic definition for similarity measures. Comparison between the previous similarity measures and the proposed similarity measure indicates that the proposed similarity measure does not provide any counterintuitive cases. Moreover, it is demonstrated that the proposed similarity measure is capable of discriminating difference between patterns
The generalized dice similarity measures for multiple attribute decision making with hesitant fuzzy linguistic information
In this paper, we shall present some novel Dice similarity measures of hesitant fuzzy linguistic term sets and the generalized Dice similarity measures of hesitant fuzzy linguistic term sets and indicate that the Dice similarity measures and asymmetric measures (projection measures) are the special cases of the generalized Dice similarity measures in some parameter values. Then, we propose the generalized Dice similarity measures-based multiple attribute decision making models with hesitant fuzzy linguistic term sets. Finally, a practical example concerning the evaluation of the quality of movies is given to illustrate the applicability and advantage of the proposed generalized Dice similarity measure
Decision making with both diversity supporting and opposing membership information
Online big data provides large amounts of decision information
to decision makers, but supporting and opposing information are
present simultaneously. Dual hesitant fuzzy sets (DHFSs) are useful
models for exactly expressing the membership degree of both
supporting and opposing information in decision making.
However, the application of DHFSs requires an improved distance
measure. This paper aims to improve distance measure models
for DHFSs and apply the new distance models to generate a technique
for order preference by similarity to an ideal solution
(TOPSIS) method for multiple attribute decision making (MADM)
The generalized dice similarity measures for multiple attribute decision making with hesitant fuzzy linguistic information
In this paper, we shall present some novel Dice similarity measures of hesitant fuzzy linguistic term sets and the generalized Dice similarity measures of hesitant fuzzy linguistic term sets and indicate that the Dice similarity measures and asymmetric measures (projection measures) are the special cases of the generalized Dice similarity measures in some parameter values. Then, we propose the generalized Dice similarity measures-based multiple attribute decision making models with hesitant fuzzy linguistic term sets. Finally, a practical example concerning the evaluation of the quality of movies is given to illustrate the applicability and advantage of the proposed generalized Dice similarity measure
Plithogeny, Plithogenic Set, Logic, Probability, and Statistics
In this book we introduce for the first time, as generalization of dialectics and neutrosophy, the philosophical concept called plithogeny. And as its derivatives: the plithogenic set (as generalization of crisp, fuzzy, intuitionistic fuzzy, and neutrosophic sets), plithogenic logic (as generalization of classical, fuzzy, intuitionistic fuzzy, and neutrosophic logics), plithogenic probability (as generalization of classical, imprecise, and neutrosophic probabilities), and plithogenic statistics (as generalization of classical, and neutrosophic statistics).
Plithogeny is the genesis or origination, creation, formation, development, and evolution of new entities from dynamics and organic fusions of contradictory and/or neutrals and/or non-contradictory multiple old entities.
Plithogenic Set is a set whose elements are characterized by one or more attributes, and each attribute may have many values.
An attribute’s value v has a corresponding (fuzzy, intuitionistic fuzzy, or neutrosophic) degree of appurtenance d(x, v) of the element x, to the set P, with respect to some given criteria.
In order to obtain a better accuracy for the plithogenic aggregation operators in the plithogenic set/logic/probability and for a more exact inclusion (partial order), a (fuzzy, intuitionistic fuzzy, or neutrosophic) contradiction (dissimilarity) degree is defined between each attribute value and the dominant (most important) attribute value.
The plithogenic intersection and union are linear combinations of the fuzzy operators tnorm and tconorm, while the plithogenic complement/inclusion/equality are influenced by the attribute values’ contradiction (dissimilarity) degrees.
Formal definitions of plithogenic set/logic/probability/statistics are presented into the book, followed by plithogenic aggregation operators, various theorems related to them, and afterwards examples and applications of these new concepts in our everyday life
Improved Knowledge Measures for q-Rung Orthopair Fuzzy Sets
The q-rung orthopair fuzzy set (qROFS) defined by Yager is a generalization of Atanassov intuitionistic fuzzy set (IFS) and Pythagorean fuzzy sets (PyFSs). In this paper, we define the knowledge measure for qROFS by using the cosine inverse function. The information precision and information content are two facets of knowledge measure. Both facets of knowledge measure are considered. The properties of knowledge measure and their graphical explanations are discussed. An application of the knowledge measure in multi-attribute group decision-making (MAGDM) problem under the confidence level approach is given. A numerical example of the selection of renewable energy sources is discussed
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