• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.98-103
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• 1997

Defuzzification plays a great role in fuzzy control system. Defuzzification is a process which maps from a space defined over an output universe of discourse into a space of nonfuzzy(crisp) number. But, it's impossible to convert a fuzzy set into a numeric value without losing some information during defuzzification. Also it's very hard to find a number that best represents a fuzzy set. Many methods have been used for defuzzification but most of then were problem dependent. There has been no rule which guides how to select a method that is suitable to solve given problem. Here, we have investigated most widely used methods and we have analyzed their characteristics and evaluated them. D. Driankov and Mizumoto have suggested 5 criteria which the‘ideal’defuzzification method should satisfy. But, they didn't considered about control action. Output fuzzy set if not only a fuzzy set but also a sequence of control action. We suggested 4 new criteria which describe sequence of cont ol action from some experiments. In addition, we have compared each method in simple adaptive fuzzy control. COG(Center of Gravity), or COS(Center of Sums) methods were successful in fuzzy control. However, at transition region, MOM(Mean of Maxima) was best among others in adaptive fuzzy control.

• Honam Mathematical Journal
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• v.31 no.3
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• pp.335-349
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• 2009

Soft set theory by Molodtsov is applied to ideals in BCC-algebras. The notion of soft BCC-ideals of soft BCC-algebras and idealistic soft BCC-algebras are introduced, and several examples are provided. Relations between a fuzzy BCC-ideal and an idealistic soft BCC-algebra are given, and the characterization of idealistic soft BCC-algebras is established.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.2
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• pp.182-186
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• 2002

We investigate the quotient structure of a product BCK-algebra and fundamental homomorphism theorem and show their some properties.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.6
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• pp.1087-1092
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• 2009

This paper presents a method for improvement of capacity payment in CBP(cost based pool) market. Capacity payments have been used as common mechanisms in various pools for compensating generators recognized to serve a for reliability purpose. Ideal pricing for capacity reserves by definition achieves a balance between economic efficiency and investment incentives. That is, prices must be kept close to costs, but not so low as to discourage investment. However, the price set is not easy. This paper concludes with market design recommendations that apply fuzzy theory for improvement of capacity payment. Following this model, market participants decided on their own based on their forecast to the market demand and the payment for it.

• Korean Journal of Mathematics
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• v.21 no.2
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• pp.171-180
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• 2013

We consider a fuzzy semigroup S in a right (or left) reductive semigroup X such that $S(k)=1$ for some $k{\in}X$ and find a faithful representation (or anti-representation) of S by transformations of S. Also we show that a fuzzy semigroup S in a weakly reductive semigroup X such that $S(k)=1$ for some $k{\in}X$ is isomorphic to the semigroup consisting of all pairs of inner right and left translations of S and that S can be embedded into the semigroup consisting of all pairs of linked right and left translations of S with the property that S is an ideal of the semigroup.

• Structural Engineering and Mechanics
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• v.26 no.5
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• pp.517-544
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• 2007

This study shows a fuzzy tuning scheme to fuzzy sliding mode controller (FSMC) for seismic isolation of earthquake-excited structures. The sliding surface can rotate in the phase plane in such a direction that the seismic isolation can be improved. Since ideal sliding mode control requires very fast switch on the input, which can not be provided by real actuators, some modifications to the conventional sliding-mode controller have been proposed based on fuzzy logic. A superior control performance has been obtained with FSMC to deal with problems of uncertainty, imprecision and time delay. Furthermore, using the fuzzy moving sliding surface, the excellent system response is obtained if comparing with the conventional sliding mode controller (SMC), as well as reducing chattering effect. For simulation validation of the proposed seismic response control, 16-floor tall building has been considered. Simulations for six different seismic events, Elcentro (1940), Hyogoken (1995), Northridge (1994), Takochi-oki (1968), the east-west acceleration component of D$\ddot{u}$zce and Bolu records of 1999 D$\ddot{u}$zce-Bolu earthquake in Turkey, have been performed for assessing the effectiveness of the proposed control approach. Then, the simulations have been presented with figures and tables. As a result, the performance of the proposed controller has been quite remarkable, compared with that of conventional SMC.

• Proceedings of the KIEE Conference
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• 2003.04a
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• pp.248-252
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• 2003

This paper has a control method proposed for the effective self-tuning fuzzy speed control based on neural network of the induction motor indirect vector control. The vector control of an induction motor provides the decoupled control of the rotor flux magnitude and the torque producing current to performance is desirable. But, the drive performance often degrades for the machine parameter variations and its condition give rise to coupling of flux and torque current. The fuzzy speed control of an induction motor has the robustness about machine parameter variations compared with conventional PID speed control in a way. That proved to be some waf from the true. The purpose of this paper is to improve the adaptation by offering self-turning function to fuzzy speed controller. In this paper, the adaptive mechanism of fuzzy speed control in used ANN(Artificial Neural Network) technique is applied in an IFO induction machine drive, such that the machine can follow a reference model (an ideal field oriented machine) to achieve desired speed. In this paper proved the self-turning method of fuzzy controller has the robustness about parameter variation and the wide range of adaptation by simulation.

• Nuclear Engineering and Technology
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• v.53 no.3
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• pp.851-859
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• 2021

Small PWR can be used for power generation and heating. Considering that small PWR has the characteristics of flexible operating conditions and complex operating environment, the controller designed based on single power level is difficult to achieve the ideal control of small PWR in the whole range of core power range. To solve this problem, a flexible switching controller based on fuzzy controller and LQG/LTR controller is designed. Firstly, a core fuzzy multi-model suitable for full power range is established. Then, T-S fuzzy rules are designed to realize the flexible switching between fuzzy controller and LQG/LTR controller. Finally, based on the core power feedback principle, the core flexible switching control system of small PWR is established and simulated. The results show that the flexible switching controller can effectively control the core power of small PWR and the control effect has the advantages of both fuzzy controller and LQG/LTR controller.

• Smart Structures and Systems
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• v.31 no.1
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• pp.69-78
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• 2023

The purpose of this paper is to develop a scalable grey predictive controller with unavoidable random delays. Grey prediction is proposed to solve problems caused by incorrect parameter selection and to eliminate the effects of dynamic coupling between degrees of freedom (DOFs) in nonlinear systems. To address the stability problem, this study develops an improved gray-predictive adaptive fuzzy controller, which can not only solve the implementation problem by determining the stability of the system, but also apply the Linear Matrix Inequality (LMI) law to calculate Fuzzy change parameters. Fuzzy logic controllers manipulate robotic systems to improve their control performance. The stability is proved using Lyapunov stability theorem. In this article, the authors compare different controllers and the proposed predictive controller can significantly reduce the vibration of offshore platforms while keeping the required control force within an ideal small range. This paper presents a robust fuzzy control design that uses a model-based approach to overcome the effects of modeling errors. To guarantee the asymptotic stability of large nonlinear systems with multiple lags, the stability criterion is derived from the direct Lyapunov method. Based on this criterion and a distributed control system, a set of model-based fuzzy controllers is synthesized to stabilize large-scale nonlinear systems with multiple delays.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.7 no.3
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• pp.87-95
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• 2008

The purpose of this study is to provide a decision support to select an appropriate rapid prototyping(RP) machine that suits the application of a part. Selection factors include concept model, form/fit/functional model, pattern model for molding, material property, build time and part cost that greatly affect the performance of RP machines. However, the selection of a RP is not an easy decision because they are uncertain and vague. For this reason, the aim of this research is to propose hybrid multiple attribute decision making approaches to effectively evaluate RP machines. In addition, because subjective considerations are relevant to selection decision, a fuzzy logic approach is adopted. The proposed selection procedure consists of several steps. First, we identify RP machines that the users consider. After constructing the evaluation criteria, we calculate the weights of the criteria by applying the fuzzy Analytic Hierarchy Process(AHP) method. Finally, we construct the fuzzy Technique of Order Preference by Similarity to Ideal Solution(TOPSIS) method to achieve the ranking order of all machines providing the decision information for the selection of RP machines.