• Korean Journal of Mathematics
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• v.17 no.4
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• pp.361-374
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• 2009

We characterize a fuzzy partial order relation using its level set, find sufficient conditions for the image of a fuzzy partial order relation to be a fuzzy partial order relation, and find sufficient conditions for the inverse image of a fuzzy partial order relation to be a fuzzy partial order relation. Also we define a fuzzy lattice as fuzzy relations, characterize a fuzzy lattice using its level set, show that a fuzzy totally ordered set is a distributive fuzzy lattice, and show that the direct product of two fuzzy lattices is a fuzzy lattice.

• Korean Journal of Mathematics
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• v.14 no.1
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• pp.71-77
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• 2006

We find the ${\epsilon}$-fuzzy equivalence relation generated by the union of two ${\epsilon}$-fuzzy equivalence relations on a set, find the ${\epsilon}$-fuzzy equivalence relation generated by a fuzzy relation on a set, and find sufficient conditions for the composition ${\mu}{\circ}{\nu}$ of two ${\epsilon}$-fuzzy equivalence relations ${\mu}$ and ${\nu}$ to be the ${\epsilon}$-fuzzy equivalence relation generated by ${\mu}{\cup}{\nu}$. Also we study fuzzy partitions of ${\epsilon}$-fuzzy equivalence relations.

• Korean Journal of Mathematics
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• v.15 no.1
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• pp.59-69
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• 2007

We define an extended fuzzy equivalence relation, discuss some basic properties of extended fuzzy equivalence relations, find the extended fuzzy equivalence relation generated by a fuzzy relation in a set, and give some lattice theoretic properties of extended fuzzy equivalence relations.

• Korean Journal of Mathematics
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• v.11 no.2
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• pp.169-175
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• 2003

We find a G-fuzzy equivalence relation generated by the union of two G-fuzzy equivalence relations in a set, find a G-fuzzy equivalence relation generated by a fuzzy relation in a set, and find sufficient conditions for the composition ${\mu}{\circ}{\nu}$ of two G-fuzzy equivalence relations ${\mu}$ and ${\nu}$ to be a G-fuzzy equivalence relation generated by ${\mu}{\cup}{\nu}$.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.377-382
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• 2021

A fuzzy relation between X and Y as fuzzy subset of X × Y was proposed by Zadeh. Subsequently, several researchers have applied the notion of fuzzy subsets to various branches of mathematics and computer sciences. Murali an Nemitz have studied fuzzy relations connected with fuzzy equivalence relations and fuzzy functions. Ounalli and Jaoua defined a fuzzy difunctional relation on a set. difunctional relations are versatile mathematical tool, which can be used in software design and in database theory. Their work have revealed the usefulness of difunctional relations in program specification and in defining program correctness. The main goal of this paper is to define a fuzzy deterministic relation on a set, characterize the fuzzy deterministic relation as its level subsets and investigate some properties in connection with fuzzy deterministic relation. In particular we prove that a fuzzy relation R is fuzzy deterministic iff R is a fuzzy function.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.408-411
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• 1993

In this paper, we propose the Fuzzy Neural Controller with a Self-Organizing Map based on the fuzzy relation neuron. The fuzzy ndes expressing the input-output relation of the system are obtained by using the fuzzy relation neuron and updated automatically by means of the generalized delta rule. Also, the proposed method has a capability to express the knowledge acquired from the input-output data in form of fuzzy inferences rules. The learning algorithm of this fuzzy relation neuron is described. The effectiveness of the proposed fuzzy neural controller is illustrated by applying it to a number of test data sets.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.3
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• pp.208-214
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• 2013

Bandler and Kohout investigated the solvability of fuzzy relation equations with inf-implication compositions in complete lattices. Perfilieva and Noskova investigated the solvability of fuzzy relation equations with inf-implication compositions in BL-algebras. In this paper, we investigate various solutions of fuzzy relation equations with inf-implication compositions in pseudo BL-algebras.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2000.11a
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• pp.25.2-31
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• 2000

Recently, Fuzzy theory has been applied in evaluation problem. Fuzzy evaluation based on Fuzzy theory can accommodate fuzziness of judgement with people through introducing Fuzzy measure. Representative Fuzzy evaluation is Fuzzy Integral using Fuzzy measure. Definite methodology using Fuzzy Integral HFI(Hierarchical Fuzzy Integrals), HFEA(Hierarchical Fuzzy Evaluation Algorithm), HFP(Hierarchical Fuzzy Process), etc. In this paper, we deal with problem identifying evaluation value using Fuzzy Relation Equation at these Fuzzy evaluation. We verify relation between Input data and Output data through @-operation and apply this to HFP. And that we verify evaluation value which objects of evaluation are able to possess.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.3
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• pp.153-164
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• 2011

Equivalence relations and mappings for crisp sets are very well known. This paper attempts an investigation of equivalence relations and mappings for fuzzy sets. We list some concepts and results related to fuzzy relations. We give some examples corresponding to the concept of fuzzy equality and fuzzy mapping introduced by Demirci [1]. In addition, we introduce the notion of preimage and quotient of fuzzy equivalence relations. Finally, we investigate relations between a fuzzy equivalence relation and a fuzzy mapping.

• Korean Journal of Mathematics
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• v.14 no.2
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• pp.241-248
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• 2006

We define a G-fuzzy congruence, which is a generalized fuzzy congruence, and characterize the G-fuzzy congruence generated by a left and right compatible fuzzy relation on a semigroup.