• 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.

• Journal of the Korean Operations Research and Management Science Society
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• v.12 no.2
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• pp.21-35
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• 1987

This paper presents a method for propagating fuzzy concepts through fuzzy IF-THEN-ELSE rules. A fuzzy IF-THEN-ELSE rule consists of a set of fuzzy condition and conclusion pairs. These pairs assumed to contain informations about a fuzzy mapping from fuzzy concepts of condition parts to the fuzzy concepts of conclusion parts. Conventionally, vectors are used to define fuzzy concepts and matrices are used to define a fuzzy mapping between fuzzy conditions and conclusions. This approach, however, does not satisfy the existing condition property, i.e., when a fuzzy input data exactly matches to a fuzzy condition, fuzzy output data should be mapped to a corresponding fuzzy conclusion. Alternatively, we propose a parameterized approach in which every fuzzy concept is described by a parameterized standard function, including fuzzy conditions and fuzzy conclusions. A fuzzy IF-THEN-ELSE rule takes the parameterized fuzzy concept as an input, and produces a standard function with new parameters as an output. New parameters are determined by a parameterwise interpolation. That is, each output parameters are determined by interpolating parameters of the same class contained in fuzzy conclusions. Obviously, the proposed scheme always satisfies the existing condition property.

• Journal of applied mathematics & informatics
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• v.27 no.3_4
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• pp.795-807
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• 2009

The concepts of fuzzy pseudo ideals (resp. fuzzy pseudo p-ideals, associative fuzzy pseudo ideals, fuzzy pseudo q-ideals and fuzzy pseudo a-ideals) in a pseudo BCI-algebra are introduced, and related properties are investigated. Conditions for a fuzzy pseudo ideal to be a fuzzy pseudo p-ideal (resp. fuzzy pseudo q-ideal) are provided. A characterization and properties of an associative fuzzy pseudo ideal are given.

• Korean Journal of Mathematics
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• v.16 no.3
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• pp.403-412
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• 2008

We define the operations ${\vee}$ and ${\wedge}$ for fuzzy sets in a lattice, characterize fuzzy sublattices in terms of ${\vee}$ and ${\wedge}$, develop some properties of the distributive fuzzy sublattices, and find the fuzzy ideal generated by a fuzzy subset in a lattice and the fuzzy dual ideal generated by a fuzzy subset in a lattice.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.268-271
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• 2008

In this paper, we introduce the notions of a fuzzy convergence of sequences, fuzzy Cauchy sequence and the related fuzzy completeness on a fuzzy normed linear space. And we investigate some properties relative to fuzzy normed linear spaces. In particular, we prove an equivalent conditions that a fuzzy norm defined on a ordinary normed linear space is fuzzy complete.

• Communications of the Korean Mathematical Society
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• v.26 no.4
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• pp.531-542
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• 2011

The notion of bipolar fuzzy a-ideals of BCI-algebras is introduced, and their properties are investigated. Relations between bipolar fuzzy subalgebras, bipolar fuzzy ideals and bipolar fuzzy a-ideals are discussed. Conditions for a bipolar fuzzy ideal to be a bipolar fuzzy a-ideal are provided. Characterizations of bipolar fuzzy a-ideals are given. Using a finite collection of a-ideals, a bipolar fuzzy a-ideal is established.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1270-1273
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• 1993

Fuzzy optimal control is considered. An optimal sequence of controls is sought best satisfying fuzzy constraints on the controls and fuzzy goals on the states (outputs), with a fuzzy system under control Control over a fixed and specified, implicitly specified, fuzzy, and infinite termination time is discussed. For computational efficiency a small number of reference fuzzy staters and controls is to be assumed by which fuzzy controls and stated are approximated. Optimal control policies reference fuzzy states are determined as a fuzzy relation used, via the compositional rule of inference, to derive an optimal control. Since this requires a large number of overlapping reference fuzzy controls and states implying a low computational efficiency, a small number of nonoverlapping reference fuzzy states and controls is assumed, and then interpolative reasoning is used to infer an optimal fuzzy control for a current fuzzy state.

• Korean Journal of Mathematics
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• v.23 no.4
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• pp.557-569
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• 2015

We dene a fuzzy lattice as a fuzzy relation, develop some basic properties of the fuzzy lattice, show that the operations of join and meet in fuzzy lattices are isotone and associative, characterize a fuzzy lattice by its level set, and show that the direct product of two fuzzy lattices is a fuzzy lattice.

• Honam Mathematical Journal
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• v.36 no.3
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• pp.555-568
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• 2014

On the basis of the theory of a falling shadow and fuzzy sets, the notion of a falling fuzzy BCI-commutative ideal of a BCI-algebra is introduced. Relations between falling fuzzy BCI-commutative ideals and falling fuzzy ideals are given. Relations between fuzzy BCI-commutative ideals and falling fuzzy BCI-commutative ideals are provided. Characterizations of a falling fuzzy BCI-commutative ideal are established, and conditions for a falling fuzzy (closed) ideal to be a falling fuzzy BCI-commutative ideal are considered.

• Bulletin of the Korean Mathematical Society
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• v.39 no.2
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• pp.185-198
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• 2002

We Consider the fuzzification of sub-implicative ideals in BCI-algebras, and investigate some related properties. We give conditions for a fuzzy ideal to be a fuzzy sub-implicative ideal. we show that (1) every fuzzy sub-implicative ideal is a fuzzy ideal, but the converse is not true, (2) every fuzzy sub-implicative ideal is a fuzzy positive implicative ideal, but the converse is not true, and (3) every fuzzy p-ideal is a fuzzy sub-implicative ideal, but the converse is not true. Using a family of sub-implicative ideals of a BCI-algebra, we establish a fuzzy sub-implicative ideal, and using a level set of a fuzzy set in a BCI-algebra, we give a characterization of a fuzzy sub-implicative ideal.