Top PDF A Generalized Interval Valued Intuitionistic Fuzzy Sets theory

A Generalized Interval Valued Intuitionistic Fuzzy Sets theory

A Generalized Interval Valued Intuitionistic Fuzzy Sets theory

In this paper, a novel generalized interval-valued intuitionistic fuzzy sets (GIVIFS) is presented, which is the generalization of conventional intuitionistic fuzzy sets (IFS) and interval-valued intuitionistic fuzzy sets (IVIFS). By analyzing the degree of hesitancy, this paper introduces generalized interval-valued intuitionistic fuzzy sets with parameters (GIVIFSP). And then, it is proved that GIVIFS is a closed algebraic system as IFS and IVIFS.

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A Comparative Analysis on Euclidean Measure In Intuitionistic Fuzzy Set And Interval-Valued Intuitionistic Fuzzy Set

A Comparative Analysis on Euclidean Measure In Intuitionistic Fuzzy Set And Interval-Valued Intuitionistic Fuzzy Set

Euclidean distance is a one of the most commonly used distance measures that has been used to solve many theoretical and practical issues in fuzzy problem. This method is widely use in many fields such as communication [2], engineering [3], chemistry [4], biology [5] and many mathematical specifications such as optimization [6], discrete mathematics [7], statistics [8],operation research [9] and fuzzy mathematics [10] [11]. In fuzzy set theory, Euclidean distance is applied to calculate distances between fuzzy numbers or sets and as a method for decision-making in a situation where two fuzzy sets or fuzzy numbers appear at the same time. Usually, Euclidean distance is applied in a set of discrete fuzzy numbers or values in interval form.
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Interval–valued Fuzzy Bridges and Interval–valued Fuzzy Cutnodes

Interval–valued Fuzzy Bridges and Interval–valued Fuzzy Cutnodes

Bridges and cutnodes is a very important concept in graph theory. Rosenfeld [35] obtained fuzzy analogs of bridges and cutnodes. Further it was studied by Bhattacharya [9]. Again Sunitha and Vijayakumar studied about the properties of fuzzy bridges and fuzzy cutnodes [44]. It was also studied by Mordeson and Nair [20]. Strength of the paths in IVFGs were discussed by Rashmanlou and Pal [31]. Again it was studied by Akram, Yousaf and Dudek [7]. Akram and Alsheri defined intuitionistic fuzzy bridges and intuitionistic fuzzy cutnodes in [3]. Again Akram and Farooq defined bipolar fuzzy bridges and bipolar fuzzy cutnodes in [6]. Bipolar fuzzy bridges and bipolar fuzzy cutnodes were also characterized by Mathew, Sunitha and Anjali [17].
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Boxdot and Star Products on Interval   Valued Intuitionistic Fuzzy Graphs

Boxdot and Star Products on Interval Valued Intuitionistic Fuzzy Graphs

interval-valued intuitionistic fuzzy subsets of and respectively. Then Boxdot product of the two strong interval -valued intuitionistic fuzzy graphs and = is defined as a pair 2where 1 2= 1 2 , 1 2 , 1 2 , 1 2 and 1 2= 1 2 , 1 2 , 1 2 , 1 2 are interval-valued intuitionistic fuzzy sets on and

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Regular ⍺ Generalized Open Sets In Intuitionistic Fuzzy Topological Spaces

Regular ⍺ Generalized Open Sets In Intuitionistic Fuzzy Topological Spaces

The theory of fuzzy sets was introduced by Zadeh [10] in 1965. Later, Chang [2] proposed fuzzy topology in 1967. The concept of intuitionistic fuzzy sets, introduced by Atanassov [1] is a generalization of fuzzy sets. Using the notion of intuitionistic fuzzy sets, Coker [3] introduced the notion of intuitionistic fuzzy topological spaces in 1997.In this paper, we introduce intuitionistic fuzzy regular α generalized open set. We investigate some of their properties. We also introduce intuitionistic fuzzy regular α T 1/2 space and obtain some characterizations and several preservation theorems.
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Properties of interval valued intuitionistic (S,T) – Fuzzy graphs

Properties of interval valued intuitionistic (S,T) – Fuzzy graphs

fuzzy graphs, strong intuitionistic fuzzy graphs, bipolar fuzzy graphs, as well as certain types of vague graphs and vague hyper- graphs. Borzooei et al. [10 e16,25e27] studied domination, degree of vertices, new concepts of vague graphs, and bipolar fuzz graphs. Complete interval-valued fuzzy graphs were investigated by Rashmanlou and Jun [22] . Pal and Rashmanlou [20] studied irreg- ular interval-valued fuzzy graphs, de fined antipodal interval- valued fuzzy graphs [23] , and balanced interval-valued fuzzy graphs [24] . Samanta et al. [31 e34] introduced fuzzy planar graphs, m-step fuzzy competition graphs, fuzzy k-competition and p- competition graphs, and showed some results on bipolar fuzzy sets and bipolar fuzzy intersection graphs. In this paper, three new types of product operations (direct, lexicographic, and strong) of interval-valued intuitionistic (S,T) efuzzy graphs are defined. We introduce the concept of regular and totally regular interval valued intuitionistic (S,T) efuzzy graphs. Busy vertices and free vertices in interval valued intuitionistic (S,T) efuzzy graphs are defined, and their image under an isomorphism is studied.
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Study on the Development of Decision Making Using Intuitionistic Fuzzy Set (IFS) and Interval Valued Intuitionistic Fuzzy Set (IVIFS)

Study on the Development of Decision Making Using Intuitionistic Fuzzy Set (IFS) and Interval Valued Intuitionistic Fuzzy Set (IVIFS)

Cenigiz Kahraman gave very useful description about MCDM in his book on Fuzzy Multicriteria Decision Making [35]. In his book he explained MCDM problems with two basic approaches: multiple attribute decision making (MADM) and multiple objective decision making (MODM). MADM problems are distinguished from MODM problems, which involve the design of a ―best‖ alternative by considering the tradeoffs within a set of interacting design constraints. MADM refers to making selections among some courses of action in the presence of multiple, usually conflicting, attributes. In MODM problems, the number of alternatives is effectively infinite, and the tradeoffs among design criteria are typically described by continuous functions. MADM approaches can be viewed as alternative methods for combining the information in a problem‘s decision matrix together with additional information from the decision maker to determine a final ranking, screening, or selection from among the alternatives. Besides the information contained in the decision matrix, all but the simplest MADM techniques require additional information from the decision maker to arrive at a final ranking, screening, or selection. It has been widely recognized that most decisions made in the real world take place in an environment in which the goals and constraints, because of their complexity, are not known precisely, and thus, the problem cannot be exactly defined or precisely represented in a crisp value (Bellman and Zadeh, 1970). Bellman and Zadeh (1970) and Zimmermann (1978) introduced fuzzy sets into the MCDM field. They cleared the way for a new family of methods to deal with problems that had been inaccessible to and unsolvable with standard MCDM techniques. Bellman and Zadeh (1970) introduced the first approach regarding decision making in a fuzzy environment.
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On Generalized Interval Valued Fuzzy Soft Matrices

On Generalized Interval Valued Fuzzy Soft Matrices

and Abdul Razak Salleh [14] introduced generalized interval valued fuzzy soft set (GIVFSS). In their generalization of FSS, they attached a degree with the parameterization of fuzzy sets in defining an IVFSS. They discussed various operations and properties of GIVFSS. Some of these are GIVFS subset, GIVFS equal set, generalized null interval valued fuzzy soft set (GNIVFS), generalized absolute interval valued fuzzy soft set (GAIVFS), compliment of GIVFSS, union of GIVFSS’s and intersection of GIVFSS’s. They defined AND and OR operations on GIVFSS and similarity measure of two GIVFSS’s. They also give some applications of GIVFSS in DM problem and medical diagnosis.
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Quasi-arithmetic means and OWA functions in interval-valued and Atanassov's intuitionistic fuzzy set theory

Quasi-arithmetic means and OWA functions in interval-valued and Atanassov's intuitionistic fuzzy set theory

In this paper we propose an extension of the well- known OWA functions introduced by Yager to interval-valued (IVFS) and Atanassov’s intuition- istic (AIFS) fuzzy set theory. We first extend the arithmetic and the quasi-arithmetic mean using the arithmetic operators in IVFS and AIFS theory and investigate under which conditions these means are idempotent. Since on the unit interval the construc- tion of the OWA function involves reordering the input values, we propose a way of transforming the input values in IVFS and AIFS theory to a new list of input values which are now ordered.
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Some Generalized Einstein Aggregation Operators Based on the Interval-Valued Intuitionistic Fuzzy Numbers and Their Application to Group Decision Making

Some Generalized Einstein Aggregation Operators Based on the Interval-Valued Intuitionistic Fuzzy Numbers and Their Application to Group Decision Making

All the above aggregation operators are based on the algebraic operational rules of IVIFNs, and the keys of the algebraic operations are Algebraic product and Algebraic sum, which are one type of operations that can be chosen to model the intersection and union of IVIFNs. In general, a general T -norm and T - conorm can be used to model the intersection and union of IVIFNs [32,33]. Wang and Liu [34] proposed the intuitionistic fuzzy Einstein aggregation operators based on Einstein operations which meet the typical T -norm and T -conorm and have the same smooth approximations as the algebraic operators such as the Intuitionistic Fuzzy Einstein Weighted Geometric op- erator (IFEWG) and the Intuitionistic Fuzzy Einstein Ordered Weighted Geometric operator (IFEOWG), and established some general properties of these oper- ators such as idempotency, commutativity, and mono- tonicity. Wang and Liu [35] proposed the Intuitionistic Fuzzy Einstein Weighted Averaging operator (IFEWA) and the Intuitionistic Fuzzy Einstein Ordered Weighted Averaging operator (IFEOWA), and studied various properties of these operators and analyzed the relations between the existing intuitionistic fuzzy aggregation operators and them. Maris and Iliadis [36] further explained the advantages of Einstein operations by using some T -norms to unify the risk indices and to produce a unied means of risk measure. The algebraic T -norm estimated the risky areas under average rainfall conditions, and the Einstein T -norm oered a good approach for an overall evaluation. The computer system has proven its ability to work more eectively compared to the older methods.
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Interval-valued intuitionistic fuzzy $k$-ideal in semi-rings

Interval-valued intuitionistic fuzzy $k$-ideal in semi-rings

The notion of fuzzy was introduced by [13] in 1965. Atanassov [1] introduced the concept of intuitionistic fuzzy sets in 1986. Atanassov et al.[2] introduced the concept of interval-valued intuitionistic fuzzy sets which is a generaliza- tion of both intuitionistic fuzzy sets and interval-valued fuzzy sets. Several mathematicians applied the concept of interval- valued intuitionistic fuzzy sets to algebraic structures. Biswas [5] studied on Rosenfeld’s fuzzy subgroups with interval- valued membership function. Das and Dutta [6] developed the concept of extension of fuzzy ideals in semirings. Dutta and Biswas [7–10] introduced and studied some properties of fuzzy prime, fuzzy semi-prime, fuzzy completely prime ide- als in semiring. Balasubramanian and Raja [3, 4] introduced intuitionistic fuzzy k-ideal and interval-valued intuitionistic fuzzy ideal on semi-rings. In this paper, the cartisian product of interval-valued intuitionistic fuzzy k-ideal in semi-rings are studied. Investigate the relationship between A, B and A × B.
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An approach to interval-valued intuitionistic fuzzy decision making based on induced generalized symmetrical Choquet Shapley operator

An approach to interval-valued intuitionistic fuzzy decision making based on induced generalized symmetrical Choquet Shapley operator

Note that all of the above-mentioned interval- valued intuitionistic fuzzy averaging operators are based on the operational laws in [36]. From the following discussion, we can nd that there are some undesirable properties. In particular, these issues may lead to undesirable ranking results. Considering this case, this paper continues to study decision-making with interval-valued intuitionistic fuzzy information and develops a new procedure. To do this, an Induced Generalized Symmetrical Interval-Valued Intuitionis- tic Fuzzy Choquet-Shapley (IG-SIVIFCS) operator is presented, which is then used to calculate the com- prehensive attribute values. To address the situation where the weighting information of attributes is partly known, a model is built for determining the optimal fuzzy measure on the attribute set. The rest can be organized as follows.
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New Operations over Interval Valued Intuitionistic Hesitant Fuzzy Set

New Operations over Interval Valued Intuitionistic Hesitant Fuzzy Set

Copyright © 2014 Horizon Research Publishing All rights reserved. Abstract Hesitancy is the most common problem in decision making, for which hesitant fuzzy set can be considered as a useful tool allowing several possible degrees of membership of an element to a set. Recently, another suitable means were defined by Zhiming Zhang [1], called interval valued intuitionistic hesitant fuzzy sets, dealing with uncertainty and vagueness, and which is more powerful than the hesitant fuzzy sets. In this paper, four new operations are introduced on interval-valued intuitionistic hesitant fuzzy sets and several important properties are also studied. Keywords Fuzzy Sets, Intuitionistic Fuzzy Set, Hesitant
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Interval Valued Intuitionistic Fuzzy Bi-ideals in Gamma Near-rings

Interval Valued Intuitionistic Fuzzy Bi-ideals in Gamma Near-rings

Zadeh [18] introduced the concept of fuzzy set in 1965 and after that he also introduced the notion of interval valued fuzzy subset [17] (in short i-v fuzzy subset) in 1975, where the values of membership functions are intervals of numbers instead of a single number as in fuzzy set. The fuzzy set theory has been developed in many directions by the research scholars. Rosenfeld [14] first introduced the fuzzification of the algebraic sturctures and defined fuzzy subgroups. Jun and Kim [9] discussed interval-valued R- subgroups in terms of near-rings. Davvaz [6] introduced fuzzy ideals of near-rings with interval-valued membership functions. Thillaigovindan et al. [16] have studied interval valued fuzzy ideals and anti fuzzy ideals of near-rings. Abou-Zaid [1] proposed the concept of fuzzy sub near-rings and ideals.
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New Operations over Interval Valued Intuitionistic Hesitant Fuzzy Set

New Operations over Interval Valued Intuitionistic Hesitant Fuzzy Set

Therefore, the rest of the paper is set out as follows. In Section 2, some basic definitions related to intuitionistic fuzzy sets, hesitant fuzzy sets and interval valued intuitionistic hesitant fuzzy set are briefly discussed. In Section 3, four new operations on interval valued intuitionistic hesitant fuzzy sets have been proposed and some properties of these operations are proved. In section 4, we conclude the paper.

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Interval-valued Fuzzy Soft Matrix Theory

Interval-valued Fuzzy Soft Matrix Theory

Chetia et al and in [13,15] Rajarajeswari et al. defined intuitionistic fuzzy soft matrix and interval valued intuitionistic fuzzy soft matrix and its types. Also extended some operations and an algorithm for medical diagnosis in [14,16].In[10], Mitra Basu et al. described interval valued fuzzy soft matrix and its types.

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Interval-valued Trapezoidal Intuitionistic Fuzzy Generalized Aggregation Operators and Application to Multi-attribute Group Decision Making

Interval-valued Trapezoidal Intuitionistic Fuzzy Generalized Aggregation Operators and Application to Multi-attribute Group Decision Making

In a similar way to TIFNs, Wang [35] dened the Trapezoidal IFN (TrIFN) and Interval-Valued Trape- zoidal IFN (IVTrIFN). Both TrIFN and IVTrIFN are extensions of TIFNs. Wang and Zhang [36] investi- gated the weighted arithmetic averaging operator and weighted geometric averaging operator on TrIFNs and their applications to MADM problems. Wei [37] in- vestigated some arithmetic aggregation operators with TrIFNs and their applications to MAGDM problems. Du and Liu [38] extended the fuzzy VIKOR method with TrIFNs. Wu and Cao [39] developed some fam- ilies of geometric aggregation operators with TrIFNs and applied them to MAGDM problems. Wan and Dong [40] dened the expectation and expectant score, ordered weighted aggregation operator and hybrid aggregation operator for TrIFNs and employed them for MAGDM. Ye [41] developed the expected value method for intuitionistic trapezoidal fuzzy multicrite- ria decision-making problems. Ye [42] proposed the MAGDM method using vector similarity measures for TrIFNs. Wan [43] developed four kinds of power av- erage operator of TrIFNs, involving the power average operator, weighted power average operator of, power order weighted average operator of, and power hybrid average operator of TrIFNs. Wan [44] rstly dened some operational laws and the weighted arithmetical average operator of IVTrIFNs. Based on the score function and accurate function, an approach is pre- sented to rank IVTrIFNs. The MAGDM method using IVTrIFNs is then proposed. Wan [45] constructed non- linear fractional programming models to estimate the alternative's relative closeness. After transformation into linear programming models, the interval of the
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INTERVAL - VALUED INTUITIONISTIC FUZZY ASSIGNMENT PROBLEM WITH REPLACEMENT BASED ON FUZZY AGGREGATION

INTERVAL - VALUED INTUITIONISTIC FUZZY ASSIGNMENT PROBLEM WITH REPLACEMENT BASED ON FUZZY AGGREGATION

Atanassov and Gargov (1989) generalized the concept of instuitionistic fuzzy set (IFS) to interval - valued intuitionistic fuzzy set (IVIFS) and define some basic operational laws of IVIFS. In the corporate sector the assignment problem plays a vital role. Among researchers it has received a significant amount of attention. The costs are not known exactly in real world application. Employing fuzzy theory to model uncertainity in real problem it is assumed that the membership function of parameters are known. However in reality it is not always easy. In order to solve this problem, the best thing is to determine the uncertain as intervals. In some situations if an individual is not familiar with the problem, it is difficult to determine the exact values of the preference degrees. His views may be positive, negative and hesitative points. In this realistic situation intuitionistic fuzzy set is the useful tool to express.
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Interval Valued intuitionistic Fuzzy Homomorphism of BF-algebras

Interval Valued intuitionistic Fuzzy Homomorphism of BF-algebras

For the first time Zadeh (1965) introduced the concept of fuzzy sets and also Zadeh (1975) introduced the concept of an interval-valued fuzzy sets, which is an extension of the concept of fuzzy set. Atanassov and Gargov, 1989 introduced the notion of interval-valued intuitionistic fuzzy sets, which is a generalization of both intuitionistic fuzzy sets and interval-valued fuzzy sets. On other hand, Satyanarayana et al., (2012) applied the concept of interval-valued intuitionistic fuzzy ideals. In this paper we introduce the notion of interval-valued intuitionistic fuzzy homomorphism of BF-algebras and investigate some interesting properties.
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Similarity Measure of Interval Valued Intuitionistic Fuzzy Soft Sets of Root Type in Decision Making

Similarity Measure of Interval Valued Intuitionistic Fuzzy Soft Sets of Root Type in Decision Making

Remark 1. Atanassov’s(1986) definition of intuitionistic fuzzy set imposes a condition on and as which in turn implies that for each This goes against the spirit that and are assigned independently. As this independence criteria is more important, we relax the

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