• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.367-367
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• 2000

In this paper, an Adaptive neuro-fuzzy Inference system(ANFIS) using fuzzy min-max network(FMMN) is proposed. Fuzzy min-max network classifier that utilizes fuzzy sets as pattern classes is described. Each fuzzy set is an aggregation of fuzzy set hyperboxes. Here, the proposed method transforms the hyperboxes into gaussian membership functions, where the transformed membership functions are inserted for generating fuzzy rules of ANFIS. Finally, we applied the proposed method to the classification problem of iris data and obtained a better performance than previous works.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.2
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• pp.184-189
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• 2001

First, we find the characterization of fg-closure of a fuzzy set. Second, we study some properties of frg-closeds and fg-continuous mappings. Finaly, we introduce the concept of a fuzzy approximately continuous mapping and study its properties.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.339-346
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• 1995

In this paper we tried to apply the concepts of fuzzy set to Lie algebra, to define some concepts related to fuzzy set to discuss their properties and relations among them.

• Journal of Electrical Engineering and Technology
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• v.3 no.1
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• pp.101-107
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• 2008

We introduce an optimization of fuzzy set-fuzzy systems based on IG (Information Granules). The proposed fuzzy model implements system structure and parameter identification by means of IG and GAs. The concept of information granulation was coped with to enhance the abilities of structural optimization of the fuzzy model. Granulation of information realized with C-Means clustering helps determine the initial parameters of the fuzzy model such as the initial apexes of the membership functions in the premise part and the initial values of polynomial functions in the consequence part of the fuzzy rules. The initial parameters are adjusted effectively with the help of the GAs and the standard least square method. To optimally identify the structure and the parameters of the fuzzy model we exploit GAs with successive tuning method to simultaneously search the structure and the parameters within one individual. We also consider the variant generation-based evolution to adjust the rate of identification of the structure and the parameters in successive tuning method. The proposed model is evaluated with the performance of the conventional fuzzy model.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.234-238
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• 2004

We introduce the concepts of fuzzy (r, s)-semiopen sets and fuzzy (r, s)-semicontinuous mappings on intuitionistic fuzzy topological spaces in Sostak's sense and then we investigate some of their characteristic properties.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.360-365
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• 1995

We shall define the usual fuzzy distance between two fuzzy points in R, the set of all real, numbers, using the usual distance between two points in R. Applying the notion of this usual fuzzy distance, we construct the usual fuzzy topology for R, introduce the notions of lower, stationary and upper cover and obtain the fuzzy Heine-Borel theorem.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.2
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• pp.154-161
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• 2014

We discuss some interesting sublattices of interval-valued fuzzy subgroups. In our main result, we consider the set of all interval-valued fuzzy normal subgroups with finite range that attain the same value at the identity element of the group. We then prove that this set forms a modular sublattice of the lattice of interval-valued fuzzy subgroups. In fact, this is an interval-valued fuzzy version of a well-known result from classical lattice theory. Finally, we employ a lattice diagram to exhibit the interrelationship among these sublattices.

• Journal of the Korean Institute of Intelligent Systems
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• v.2 no.2
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• pp.34-41
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• 1992

A robust and reliable learning and reasoning mechanism is addressed based upon fuzzy set theory and fuzzy associative memories. The mechanism stores a priori an initial knowledge base via approximate learning and utilizes this information for decision-making systems via fuzzy inferencing. We called this fuzzy computer architecture a 'fuzzy hypercube' processing all the rules in one clock period in parallel. Fuzzy hypercubes can be applied to control of a class of complex and highly nonlinear systems which suffer from vagueness uncertainty. Moreover, evidential aspects of a fuzzy hypercube are treated to assess the degree of certainty or reliability together with parameter sensitivity.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.41-45
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• 2002

From the paper which it sees a strong random number generator it uses a fuzzy set from 16 method of the statistical test which is a cryptograph random number test it verifies. 16 statistical test of NIST extends in crptograph and engineering whole it is a scale which is important distinguishes the distinction incapable characterstic of the random numbers which are used. To try introduce a fuzzy set the possibility of having a more strong randomness in order to be, it strengthens the function of the random number generator.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.4
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• pp.259-266
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• 2011

We apply the concept of interval-valued fuzzy sets to theory of semigroups. We give some properties of interval-valued fuzzy ideals and interval-valued fuzzy bi-ideals, and characterize which is left [right] simple, left [right] duo and a semilattice of left [right] simple semigroups or another type of semigroups in terms of interval-valued fuzzy ideals and intervalvalued fuzzy bi-ideals.