• Korean Journal of Mathematics
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• v.10 no.2
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• pp.131-140
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• 2002

This paper is devoted to the study of the role of fuzzy proximity spaces. We define a fuzzy K-proximity space, a fuzzy R-proximity space and prove some of its properties. Furthermore, we discuss the topological structure based on these fuzzy K-proximity and fuzzy R-proximity.

• Korean Journal of Mathematics
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• v.14 no.1
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• pp.7-11
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• 2006

This paper is devoted to the study of the role of fuzzy proximity spaces. We define a fuzzy K-proximally continuous mapping based on the fuzzy K-proximity and prove some of its properties.

• Korean Journal of Mathematics
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• v.5 no.1
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• pp.35-48
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• 1997

We will define the fuzzy quasi-proximity space and investigate some properties of fuzzy quasi-proximity spaces. We will prove the existences of initial fuzzy quasi-proximity structures. From this fact, we can define subspaces and products of fuzzy quasi-proximity spaces.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.64-69
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• 1998

In this paper, we introduce the concept of the intuitionistic fuzzy proximity space as a generalization of a fuzzy proximity space, and investigate some of their properties. Also we study the relations between intuitionistic fuzzy proximity spaces and intuitionistic fuzzy topological spaces.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.1
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• pp.116-119
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• 2005

In this paper, we show that the category of intuitionistic fuzzy proximity spaces has an initial structure, and consequently a subspace structure and a product space structure of them.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.550-554
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• 1999

We will define a fuzzy quasi-proximity space and give some examples of it. We show that the family M(X, C) of all fuzzy quasi-proximities on X which induce C is nonempty. Moreover we will study the relationship between the category of fuzzy closure spaces and that of fuzzy quasi-proximity spaces.

• Korean Journal of Mathematics
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• v.6 no.1
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• pp.97-115
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• 1998

We will define a fuzzy quasi-uniform space and investigate some properties of fuzzy quasi-uniform spaces. We will show that the fuzzy bitopology and the fuzzy quasi-proximity can be induced by a fuzzy quasi-uniformity.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.2
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• pp.167-172
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• 2006

As ubiquitous devices are fast spreading, the communication problem between humans and these devices is on the rise. The use of context is important in interactive application such as handhold and ubiquitous computing. Context is not crisp data, so it is necessary to introduce the fuzzy concept. The proxity relation is represented by the degree of closeness or similarity between data objects of a scalar domain. A context manager of context-awareness system evaluates imprecise queries with the proximity relations. in this paper, a systematic proximity elicitation method are proposed. The proposed generation method is simple and systematic. It is based on the well-known fuzzy set theory and applicable to the real world applications because it has tuning parameter and weighting factor. The proposed representations of proximity relation is more efficient than the ordinary matrix representation since it reflects some properties of a proximity relation to save space. We show an experiments of quantitative calculate for the proximity relation. And we analyze the time complexity and the space occupancy of the proposed representation method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.2
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• pp.436-444
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• 2010

In this study, we introduce and discuss a concept of a granular-oriented radial basis function neural networks (GRBF NNs). In contrast to the typical architectures encountered in radial basis function neural networks(RBF NNs), our main objective is to develop a design strategy of GRBF NNs as follows : (a) The architecture of the network is fully reflective of the structure encountered in the training data which are granulated with the aid of clustering techniques. More specifically, the output space is granulated with use of K-Means clustering while the information granules in the multidimensional input space are formed by using a so-called context-based Fuzzy C-Means which takes into account the structure being already formed in the output space, (b) The innovative development facet of the network involves a dynamic reduction of dimensionality of the input space in which the information granules are formed in the subspace of the overall input space which is formed by selecting a suitable subset of input variables so that the this subspace retains the structure of the entire space. As this search is of combinatorial character, we use the technique of genetic optimization to determine the optimal input subspaces. A series of numeric studies exploiting some nonlinear process data and a dataset coming from the machine learning repository provide a detailed insight into the nature of the algorithm and its parameters as well as offer some comparative analysis.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.4
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• pp.264-269
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• 2008

In this paper, two neural networks as a material model, which are based on the state-space method, have been proposed. One outputs the rates of inelastic strain and material internal variables whereas the outputs of the other are the next state of the inelastic strain and material internal variables. Both the neural networks were trained using input-output data generated from Chaboche's model and successfully converged. The former neural network could reproduce the original stress-strain curve. The neural network also demonstrated its ability of interpolation by generating untrained curve. It was also found that the neural network can extrapolate in close proximity to the training data.