• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.10
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• pp.584-592
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• 2003

This paper presents a fuzzy-GA method for the allocation and operation of dispersed generator systems(DGs) based on load model in composite distribution systems. Groups of each individual load model consist of residential, industrial, commercial, official and agricultural load. The problem formulation considers an objective to reduce power loss of distribution systems and the constraints such as the number or total capacity of DGs and the deviation of the bus voltage. The main idea of solving fuzzy goal programming is to transform the original objective function and constraints into the equivalent multi-objectives functions with fuzzy sets to evaluate their imprecise nature for the criterion of power loss minimization, the number or total capacity of DGs and the bus voltage deviation, and then solve the problem using genetic algorithm. The method proposed is applied to IEEE 12 bus and 33 bus test systems to demonstrate its effectiveness. .

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.182-182
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• 2000

This paper proposes a self tuning fuzzy inference method by the genetic algorithm in the fuzzy-sliding mode control for a robot. Using this method, the number of inference rules and the shape of membership functions are optimized without an expert in robotics. The fuzzy outputs of the consequent part are updated by the gradient descent method. And, it is guaranteed that the selected solution become the global optimal solution by optimizing the Akaike's information criterion. The trajectory trucking experiment of the polishing robot system shows that the optimal fuzzy inference rules are automatically selected by the genetic algorithm and the proposed fuzzy-sliding model controller provides reliable tracking performance during the polishing process.

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 1998.10a
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• pp.64-68
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• 1998

This paper discusses the potential application of fuzzy set theory to the decision-making in the area of radioactive waste management. the approach proposed in this study is based on the concepts of fuzzy set theory and the hierarchical structure analysis. The linguistic variables and fuzzy numbers are used to aggregate the decision maker's subjective assessments of the decision criteria and of the decision alternatives with respect to these criteria. For each alternative, the fuzzy appropriateness index is evaluated to obtain the final score. Using total integral value method, one of methods for ranking fuzzy numbers, the fuzzy appropriateness indices are ranked. As a case problem, selection of the most suitable option for spent fuel storage is illustrated.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.29 no.4
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• pp.286-291
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• 1993

This paper presents a design method of fuzzy controller based on TSK fuzzy model. By using the proposed method, we can design fuzzy controller mathematically, which guarantees the stability of fuzzy system. We derived a theorem related to the stability of fuzzy system. In that theorem, we show that the fuzzy system has the same stable state transition matrix as we desire. The validity of the proposed method is shown through an experiment of DC motor velocity control.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2120-2122
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• 2002

There are several types of control systems that use fuzzy logic controller as a essential system component. The majority of research work on fuzzy PID controller focuses on the conventional two-input PI or PD type controller. However, fuzzy PID controller design is a complex task due to the involvement of a large number of parameters in defining the fuzzy rule base. In this paper we combined conventional PI type and PD type fuzzy controller and set the initial parameters of this controller from the conventional PID controller gains obtained by Ziegler-Nichols tuning or other coarse tuning methods. After that, by replacing some of these parameters with sing1e neurons and making them to be adjusted by back-propagation learning algorithm we designed a neuro-fuzzy controller which showed good performance characteristics in both computer simulation and actual application.

• East Asian mathematical journal
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• v.23 no.2
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• pp.175-195
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• 2007

The object of this paper is to introduce the notion of semi-compatible maps in fuzzy metric spaces, fuzzy 2-metric spaces and fuzzy 3-metric spaces and to establish three common fixed point theorems for these spaces for four self-maps. These results improve, extend and generalize the results of [16]. As an application, these results have been used to obtain translation and generalization of Grabeic's contraction principle in the new settings. All the result presented in this paper are new.

• Journal of the military operations research society of Korea
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• v.18 no.2
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• pp.66-95
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• 1992

Shortest paths are one of the simplest and most widely used concepts in deterministic networks. A decison of troops movement route can be analyzed in the network with a shortest path algorithm. But in reality, the value of arcs can not be determined in the network by crisp numbers due to imprecision or fuzziness in parameters. To account for this reason, a fuzzy network should be considered. A fuzzy shortest path can be modeled by general fuzzy mathematical programming and solved by fuzzy dynamic programming. It can be formulated by the fuzzy network with lingustic variables and solved by the Klein algorithm. This paper focuses on a revised fuzzy shortest path algorithm and an application is discussed.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1471-1474
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• 2002

In this paper, we propose a robust indirect adaptive fuzzy state feedback regulator based on Takagi-Sugeno fuzzy model. The proposed adaptive fuzzy regulator is less sensitive to singularity than the conventional one based on the feedback linearization method. Furthermore, the proposed control method is applicable to not only plants with a perfect model but also plants with an imperfect model, which causes uncertainties. We verify the global stability of the proposed method by using Lyapunov method. In order to support the achievement, the application of the proposed adaptive fuzzy regulator to the control of a nonlinear system under the external disturbance is presented and the performance was verified by some simulation result.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.2
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• pp.9-18
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• 2011

Vender prioritization process is one of the most critical tasks of production and logistics management for many companies. Determining the most critical criteria for vender prioritization process is a vital means for a purchasing company to improve its supply chain productivity. This study discuss the use of a Fuzzy analytic network process (Fuzzy ANP) model which is an efficient tool to handle the fuzziness of the data involved in deciding the preferences of different criteria which are not independent. Also, the comparison of classical ANP and Fuzzy ANP is described using simulation with triangular distribution random number generation. It is shown that Fuzzy ANP model possesses some attractive properties and could be used as an alternative to the known vender prioritization methods.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.461-467
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• 1998

Although many methods of knowledge acquisition has been developed in the expert systems field, such a need for causal knowledge acquisition has not been stressed relatively. In this respect, this paper is aimed at suggesting a causal knowledge acquisition process, and then investigate the causal knowledge-based inference process. A vehicle for causal knowledge acquisition is FCM (Fuzzy Cognitive Map), a fuzzy signed digraph with causal relationships between concept variables found in a specific application domain. Although FCM has a plenty of generic properties for causal knowledge acquisition, it needs some theoretical improvement for acquiring a more refined causal knowledge. In this sense, we refine fuzzy implications of FCM by proposing fuzzy implications of FCM by proposing fuzzy causal relationship and fuzzy partially causal relationship. To test the validity of our proposed approcach, we prototyped a causal knowledge-driven inference engine named CAKES and then experime ted with some illustrative examples.