• Bulletin of the Korean Mathematical Society
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• v.45 no.2
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• pp.207-220
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• 2008

We introduce and study the notions of supra fuzzy convergence of fuzzy filters, $s{\gamma}$-fuzzy open (closed) sets, $s{\gamma}(/TEX>$s{\gamma}^*)$-fuzzy continuous and $s{\gamma}$-fuzzy open mapping. Also. we investigate some of fundamental properties of these notions.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.4
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• pp.321-330
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• 2006

We introduce the concept of intuitionistic fuzzy weak congruence on a semiring and obtain the relation between intuitionistic fuzzy weak congruence and intuitionistic fuzzy ideal of a semiring. Also we define and investigate intuitionistic fuzzy quotient semiring of a semiring over an intuitionistic fuzzy ideal or over an intuitionistic fuzzy weak congruence.

• Journal of the Chungcheong Mathematical Society
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• v.12 no.1
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• pp.63-72
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• 1999

We introduce a concept of fuzzy linearity: A function $F:L^0(X){\rightarrow}\mathbb{R}$ is fuzzy linear if $F[({\alpha}{\wedge}f){\vee}(b{\wedge}g)]=[a{\wedge}F(f)]{\vee}[b{\wedge}F(g)]$ for $f,g{\in}L^0(X)$ and a, b > 0. We show that a fuzzy integral is fuzzy linear if the measure is fuzzy c-additive.

• Journal of applied mathematics & informatics
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• v.28 no.1_2
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• pp.99-107
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• 2010

This paper is a study about the relationships among topologies and intuitionistic fuzzy topology induced, respectively, by approximation operators and an intuitionistic fuzzy approximation operator associated with an approximation space (X, R), when the relation R on X is precisely reflexive and transitive. In particular, we consider an intuitionistic fuzzy approximation operator on an approximation space X (i.e., a set X with a reflexive and transitive relation on it), which turns out to be an intuitionistic fuzzy closure operator. This intuitionistic fuzzy closure operator gives rise to two saturated fuzzy topologies on X and it turns out that all the level topologies of one of the fuzzy topology coincide and equal to the topology analogously induced on X by a crisp approximation operator. These observations are then applied to intuitionistic fuzzy automata.

• Coupled systems mechanics
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• v.1 no.4
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• pp.345-360
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• 2012

In this article we have presented a unique representation for interval arithmetic. The traditional interval arithmetic is transformed into crisp by symbolic parameterization. Then the proposed interval arithmetic is extended for fuzzy numbers and this fuzzy arithmetic is used as a tool for uncertain finite element method. In general, the fuzzy finite element converts the governing differential equations into fuzzy algebraic equations. Fuzzy algebraic equations either give a fuzzy eigenvalue problem or a fuzzy system of linear equations. The proposed methods have been used to solve a test problem namely heat conduction problem along with fuzzy finite element method to see the efficacy and powerfulness of the methodology. As such a coupled set of fuzzy linear equations are obtained. These coupled fuzzy linear equations have been solved by two techniques such as by fuzzy iteration method and fuzzy eigenvalue method. Obtained results are compared and it has seen that the proposed methods are reliable and may be applicable to other heat conduction problems too.

• Communications of the Korean Mathematical Society
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• v.26 no.3
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• pp.349-371
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• 2011

Using the "belongs to" relation and "quasi-coincident with" relation between a fuzzy point and a fuzzy subgroup, Bhakat and Das, in 1992 and 1996, initiated general types of fuzzy subgroups which are a generalization of Rosenfeld's fuzzy subgroups. In this paper, more general notions of "belongs to" and "quasi-coincident with" relation between a fuzzy point and a fuzzy set are provided, and more general formulations of general types of fuzzy (normal) subgroups by Bhakat and Das are discussed. Furthermore, general type of coset is introduced, and related fundamental properties are investigated.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.2
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• pp.371-383
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• 1997

This paper propose a fuzzy regression method using fuzzy neural networks when a membership value is attached to each input-output pair. First, a method of linear fuzzy regression analysis is described by interpreting the reliability of each input-output pair as its membership values. Next, an architecture of fuzzy neural networks with fuzzy weights and fuzzy biases is shown. The fuzzy neural network maps a crisp input vector to a fuzzy output. A cost function is defined using the fuzzy output from the fuzzy neural network and the corresponding target output with a membership value. A learning algorithm is derived from the cost function. The derived learning algorithm trains the fuzzy neural network so that the level set of the fuzzy output includes the target output. Last, the proposed method is illustrated by computer simulations on numerical examples.

• Honam Mathematical Journal
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• v.36 no.4
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• pp.813-830
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• 2014

The notions of a (Boolean, prime, ultra, good) hesitant fuzzy filter and a hesitant fuzzy MV -filter of an MTL-algebras are introduced, and their relations are investigated. Characterizations of a (Boolean, ultra) hesitant fuzzy filter are discussed. Conditions for a hesitant fuzzy set to be a hesitant fuzzy filter, and for a hesitant fuzzy filter to be a Boolean hesitant fuzzy filter are provided.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.4
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• pp.569-573
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• 2010

We introduce the concept of fuzzy r-minimal preopen set on a fuzzy minimal space. We also introduce the concept of fuzzy r-M precontinuous mapping which is a generalization of fuzzy r-M continuous mapping, and investigate characterization of fuzzy r-M precontinuity.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.404-409
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• 1998

In this paper, we propose a learning algorithm of fuzzy neural networks with trapezoidal fuzzy weights. This fuzzy neural networks can use fuzzy numbers as well as real numbers, and represent linguistic information better than standard neural networks. We construct trapezodal fuzzy weights by the composition of two triangles, and devise a learning algorithm using the two triangular membership functions, The results of computer simulations on numerical data show that the fuzzy neural networks have high fitting ability for target output.