Abstract
In this paper, we introduce types of relations on complex fuzzy sets such as the complex fuzzy (CF) inverse relation, complex fuzzy reflexive relation, complex fuzzy symmetric relation, complex fuzzy antisymmetric relation, complex fuzzy transitive relation, complex fuzzy irreflexive relation, complex fuzzy asymmetric relation, complex fuzzy equivalence relation, and complex fuzzy-order relation. We study some basic results and particular examples of these relations. Moreover, we discuss the applications of complex fuzzy relations in Future Commission Market (FCM). We show that the introduction of CF relations to applications of FCMs can give a significant method for describing the temporal dependence between parameters of a Future Commission Market.
Highlights
Models reflecting the phenomena of real life with just choices of truth and falsehood are not enough to reflect the true reality of the problems. e explanation for this is that the models have many complications, which is why a framework needs to be built to deal with the models’ illdefined situations. ere are two ways to deal with these kinds of situations, one is to find the problems’ numerical solutions and the other is to create a numerical model
We get numerical solutions to the problems in both cases. e second is about the fuzzy set theory, which includes the theory of probability, the theory of fuzzy soft sets, the theory of intuitionist fuzzy sets, and most the theory of neutrosophic sets. e later theory for dealing with problems involving complexities is more generalized
Nisren et al introduced the concept of complex multifuzzy soft expert set (CMFSES) and discussed the application of a complex multifuzzy expert soft set in decision-making problems [4]
Summary
Models reflecting the phenomena of real life with just choices of truth and falsehood are not enough to reflect the true reality of the problems. e explanation for this is that the models have many complications, which is why a framework needs to be built to deal with the models’ illdefined situations. ere are two ways to deal with these kinds of situations, one is to find the problems’ numerical solutions and the other is to create a numerical model. E phase term of the CFS plays a crucial role in defining the functionality of the complex fuzzy set model. Ramot et al proposed that the intermittent problems or repeated-problem phenomenon be more precisely modeled using the phase term of the complex fuzzy set membership, such as describing the effect of two countries’ financial measures on each other over time. He suggested that signal processing is yet another attractive area of operation for a complex fuzzy set. We discuss the applications of complex fuzzy relations in Future Commission Merchant
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