• Journal of Korean Institute of Industrial Engineers
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• v.16 no.2
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• pp.37-44
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• 1990

In selecting the best project in multi-criteria decision making problems, the imperfect information of consequence and the vague preference of the decision maker(DM) would make the decision analysis more complex together with the conflict of several criteria. In this context, a method is proposed to deal the fuzzy information mentioned before instead of reducing it to a single representative value. And, based on the given imprecise information, projects are ranked completely or partially according to DM's vague preference. The procedure consists, for each pair of projects, of calculating the degree of supporting over all criteria and the degree of opposing with respect to each criterion for their outranking relation. Together with weights for each criterion, these indices produce fuzzy outranking relations for each pair of projects. And a complete or partial ranking of projects is obtained according to outranking degrees considering the interdependence among projects.

• Journal of IKEEE
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• v.4 no.2 s.7
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• pp.173-180
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• 2000

In this paper, we interpreted and evaluated the relation between the sensation of equilibrium and biomedical signal automatically by applying the fuzzy theory. We induced the vertigo by using the caloric test, and presented the correlation between vertigo and biomedical signal by using the quantification method. We objectively analyzed the organic relation of the biomedical signal by fuzzy rule design using the table-lookup scheme and obtained good result in recognizing the level of the sensation of equilibrium.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.47
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• pp.219-232
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• 1998

A lot of difficulties exist in analyzing the structure of a system owing to the complex and organic relations in the systems we face in reality. Focuses have been put on the research of optimal solution in a defined structure, however, on the assumption that the structure of the system has been already defined. With the grasping of the structure as the most prior condition, ISM(Interpretive Structural Modeling) and FSM(Fuzzy Structural Modeling) are suggested as solutions in this paper. ISM uses the systematic application of some elementary notions of graph theory and boolean algebra, FSM uses Fuzzy conception for representing relationship between elements. In FSM, the entries in the relation matrix are taken to value on the interval [0,1] by virtue of a fuzzy binary relation. Numeric examples are used as the actual application as follows.

• The Pure and Applied Mathematics
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• v.11 no.2
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• pp.127-132
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• 2004

$H_v$-rings first were introduced by Vougiouklis in 1990. The largest class of algebraic systems satisfying ring-like axioms is the $H_v$-ring. Let R be an $H_v$-ring and ${\gamma}_R$ the smallest equivalence relation on R such that the quotient $R/{\gamma}_R$, the set of all equivalence classes, is a ring. In this case $R/{\gamma}_R$ is called the fundamental ring. In this short communication, we study the fundamental rings with respect to the product of two fuzzy subsets.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.4
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• pp.275-280
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• 2010

Verification of efficiency in data management fuzzy entropy and similarity measure were discussed and verified by applying reliable data selection problem and numerical data similarity evaluation. In order to calculate the certainty or uncertainty fuzzy entropy and similarity measure are designed and proved. Designed fuzzy entropy and similarity are considered as dissimilarity measure and similarity measure, and the relation between two measures are explained through graphical illustration. Obtained measures are useful to the application of decision theory and mutual information analysis problem. Extension of data quantification results based on the proposed measures are applicable to the decision making and fuzzy game theory.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.177-180
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• 1996

Fuzzy control systems depend on a number of parameters such as the shape or magnitude of the fuzzy membership functions, etc. Conventional fuzzy reasoning method can not be easily applied to the multi-input multi-output(MIMO) system due to the large number of rules in the rule base. Recently Z. Cao et al have proposed a New Fuzzy Reasoning Method(NFRM) which turned out to be superior to Zadeh's FRM. We have extended the NFRM to handle the MIMO system. However, it is difficult to choose a proper relation matrix of the NFRM. Therefore, we have modified the evolution strategy(ES), which is one of the optimization algorithms, to do efficiently the tuning operation for the extended NFRM. Finally we applied the extended NFRM with the modified ES to tracking control of robot manipulator.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.3
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• pp.92-100
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• 1995

Fuzzy reasoning has been applied to analysis of traffic problems on urban arterial road. As the analysis on factors of route choice has been already carried out, its result can be used for construction of the model. Route choice rate estimation by fuzzy reasoning was discussed from its structure and accuracy. The major objective of the study is to introduce some kinds of methods with fuzzy reasoning and to make their feature obvious. First, the production system model is introduced with consideration of reality to actual travel behavior. Second, overlapping areas of fuzzy language function are investigated. Finally, process of fuzzy reasoning was also considered. Five kinds of Fuzzy reasoning are compared to investigate in relation between shapes of membership function and estimation validity.

• Korean Journal of Cognitive Science
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• v.4 no.1
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• pp.123-152
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• 1993

This paper discusses how to automatically extract fuzzy rules from given data.The fuzzy space which contain given data are fitst subdivided into a set of hypercubes.each of which contains the homogeneous data belonging to the same class, and then a fuzzy rule is defined based on the constructed hypercube.In order to dynamically agjust the size of a hypercube. the fuzzy space is to be splitted based on a center vector and then the splitted subspaces are to be merged throungh the adjacency relation.The membership functions.which are to be embedded in a fuzzy rule.are to be formed through analyzing the cummulative histogram of given data along each axis of the constructed hypercube.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.9
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• pp.1642-1651
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• 2008

The dynamic behavior of power systems is affected by the interactions between linear and nonlinear components. To analyze those complicated power systems, the linear approaches have been widely used so far. Especially, a synchronous generator has been designed by using linear models and traditional techniques. However, due to its wide operating range, complex dynamics, transient performances, and its nonlinearities, it cannot be accurately modeled as linear methods based on small-signal analysis. This paper describes an application of the Takaki-Sugeno (T-S) fuzzy method to model the synchronous generator in a single-machine infinite bus (SMIB) system. The T-S fuzzy model can provide a highly nonlinear functional relation with a comparatively small number of fuzzy rules. The simulation results show that the proposed T-S fuzzy modeling captures all dynamic characteristics for the synchronous generator, which are exactly same as those by the conventional modeling method.

• Journal of Electrical Engineering and Technology
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• v.5 no.4
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• pp.653-665
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• 2010

In this study, we introduce a neurofuzzy approach to the design of fuzzy controllers. The development process exploits key technologies of Computational Intelligence (CI), namely, genetic algorithms (GA) and neurofuzzy networks. The crux of the design methodology deals with the selection and determination of optimal values of the scaling factors of fuzzy controllers, which are essential to the entire optimization process. First, the tuning of the scaling factors of the fuzzy controller is carried out. Next, we form a nonlinear mapping for the scaling factors, which are realized by GA-based neurofuzzy networks by using a fuzzy set or fuzzy relation. The proposed approach is applied to control nonlinear systems like the inverted pendulum. Results of comprehensive numerical studies are presented through a detailed comparative analysis.