• Journal of Korean Society for Geospatial Information Science
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• v.19 no.3
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• pp.57-66
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• 2011

The purpose of this study is to research into a method of selecting the subject parcel with a change in the category of land given surveying the land alteration state focusing on the present state of road in the government-owned and public land by using the fuzzy membership function and GIS spatial analysis. It selected the old town center of Incheon Jung-gu, and the new downtown & the forest land of Gyeyang-gu as the research subject region, and carried out GIS spatial analysis on a serial cadastral map, urban planning road layer of Korea Land Information System, practical width of road layer of Road Name Address Management System & cadastral data base, and then calculated the suitable index for the subject parcel with a change in the category of land by using the fuzzy membership function with having the critical value as the area ratio of each parcel on a serial cadastral map that was incorporated into road layer or practical width of road layer. It finally selected the parcel, which is different in land category from the real land usage, as the final subject parcel for altering land category, by using the screen of visualizing the suitable index and the aerial ortho photograph. As a result of the final selection, the fuzzy GIS spatial analysis method, which was suggested in this study, is judged to be efficient in the selection period and the methodology compared to the existing manual method. It could be confirmed to be more suitable method for downtown than forest land and for the new downtown than the old town center.

• The Pure and Applied Mathematics
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• v.13 no.4 s.34
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• pp.249-254
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• 2006

Category Fuz of fuzzy sets has a similar function to the topos Set. But Category Fuz forms a weak topos. We show that supports split weakly(SSW) and with some properties, implicity axiom of choice(IAC) holds in weak topos Fuz. So weak axiom of choice(WAC) holds in weak topos Fuz. Also we show that weak extensionality principle for arrow holds in weak topos Fuz.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.12
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• pp.954-961
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• 2013

In this paper, we propose a new learning method using a variable learning to improve pattern recognition in the FCSR(Fast Commit Slow Recode) learning method of the Fuzzy ART. Traditional learning methods have used a fixed learning rate in updating weight vector(representative pattern). In the traditional method, the weight vector will be updated with a fixed learning rate regardless of the degree of similarity of the input pattern and the representative pattern in the category. In this case, the updated weight vector is greatly influenced from the input pattern where it is on the boundary of the category. Thus, in noisy environments, this method has a problem in increasing unnecessary categories and reducing pattern recognition capacity. In the proposed method, the lower similarity between the representative pattern and input pattern is, the lower input pattern contributes for updating weight vector. As a result, this results in suppressing the unnecessary category proliferation and improving pattern recognition capacity of the Fuzzy ART in noisy environments.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.3
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• pp.150-158
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• 1998

This paper describes the classification of remotely sensed image data using fuzzy neural network, whose algorithm was obtained by replacing real numbers used for inputs and outputs in the standard back propagation algorithm with fuzzy numbers. In the proposed method, fuzzy patterns, generated based on the histogram ofeach category for the training data, are put into the fuzzy neural network with real numbers. The results show that the generalization and appoximation are better than that ofthe conventional network in determining the complex boundary of patterns.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.2
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• pp.134-141
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• 2010

We introduce the category IVSet(H) of interval-valued H-fuzzy sets and show that IVSet(H) satisfies all the conditions of a topological universe except the terminal separator property. And we study some relations among IVSet (H), ISet (H) and Set (H).

• Communications of the Korean Mathematical Society
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• v.21 no.2
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• pp.211-217
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• 2006

Topos is a set-like category. In topos, the axiom of choice can be expressed as (AC1), (AC2) and (AC3). Category Fuz of fuzzy sets has a similar function to the topos Set and it forms weak topos. But Fuz does not satisfy (AC1), (AC2) and (AC3). So we define (WAC1), (WAC2) and (WAC3) in weak topos Fuz. And we show that they are equivalent in Fuz.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.463-466
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• 2003

Fuzzy set expressing category in fuzzy rating, which is a kind of psychological scaling, is dependent on situations. This paper assumes that a mapping exists between fuzzy sets expressing categories in some situation and those expressing same categories in another situation. fuzzy sets expressing categories in some situation are obtained by fuzzy sets expressing categories in another situation and the mapping between them. The usefulness of the present method is confirmed by the experiments comparing fuzzy sets obtained by the presented method with those identified directly by fuzzy rating. The normalized distance is used to compare both fuzzy sets and the experimental results show that the normalized distances between both fuzzy sets are enough small and that the presented method is useful for psychological scaling.

• Journal of applied mathematics & informatics
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• v.42 no.1
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• pp.189-197
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• 2024

The purpose of this paper is to introduce and study the new class of spaces called the fuzzy 𝛽-Volterra spaces with the help of fuzzy β-dense and fuzzy 𝛽-G_𝛿 sets. Examples are given to illustrate the concept. Some interesting characterizations of the fuzzy 𝛽-Volterra spaces are established in this paper.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.343-345
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• 2004

We propose a new category of fuzzy set-based fuzzy inference systems based on data granulation related to fuzzy space division for each variables. Data granules are viewed as linked collections of objects(data, in particular) drawn together by the criteria of proximity, similarity, or functionality. Granulation of data with the aid of Hard C-Means(HCM) clustering algorithm help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polyminial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms(GAs) and the least square method. Numerical example is included to evaluate the performance of the proposed model.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.3
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• pp.253-258
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• 2005

In this study, we introduce a new category of fuzzy inference systems based on information granulation to carry out the model identification of complex and nonlinear systems. Informal speaking, information granules are viewed as linked collections of objects (data, in particular) drawn together by the criteria of proximity, similarity, or functionality. To identify the structure of fuzzy rules we use genetic algorithms (GAs). Granulation of information with the aid of Hard C-Means (HCM) clustering algorithm help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms and the least square method (LSM). The proposed model is contrasted with the performance of the conventional fuzzy models in the literature.