• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2545-2547
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• 2000

In this paper, a hybrid fuzzy controller using genetic algorithm based on parameter estimation mode to obtain optimal control parameter is presented. First, The control input for the system in the HFC is a convex combination of the FLC's output in transient state and PID's output in steady state by a fuzzy variable, namely, membership function of weighting coefficient. Second, genetic algorithms is presented to automatically improve the performance of hybrid fuzzy controller utilizing the conventional methods for finding PID parameters and estimation mode of scaling factor. The algorithms estimates automatically the optimal values of scaling factors, PID parameters and membership function parameters of fuzzy control rules according to the rate of change and limitation condition of control input. Computer simulations are conducted to evaluate the performance of proposed hybrid fuzzy controller. ITAE, overshoot and rising time are used as a performance index of controller.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.412-416
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• 1993

In this paper, fuzzy-neural network is proposed to identify the Activated Sludge Process(A.S.P) in sewage treatment such as "IF-THEN" type fuzzy rules and using various learning methods and improved complex method, the performance index of the identified model is improved. The proposed FNN has the neural network structure of which the connection weights have particular meanings for obtaining fuzzy inference rules and for tuning membership functions. And based on the identified model, graphic simulator which can analize nonlinear characteristics of A.S.P and generate control strategy for A.S.P is being developed.developed.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1141-1144
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• 1993

For the design of multivariable fuzzy control systems the decomposition of control rules is preferable since it alleviates the complexity of the problem. In some systems, however, inference error of the Gupta's decomposition method is inevitable because of its approximate nature. In this paper, we propose a new multivariable fuzzy controller with a coordinator which can reduce the inference error of the decomposition method by using an index of applicability.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1191-1194
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• 1993

We consider a fuzzy controller corresponding to PI controller. This controller is applied to a controlled object which is a first order lag system with dead time. An antecedent part is divided into 3, 5, and 7 parts ( membership function of triangle shape ), and a consequent part into 3, 5, and 7 parts ( membership function of singleton ). In each combination of an antecedent part and a consequent one. We compare control efficiency under the performance criteria such that the overshoot is kept 20% and the ITAE index is minimized.

• 한국지능시스템학회논문지
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• 제5권1호
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• pp.21-24
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• 1995

Fuzzy inference method is introduced to order totally a partiallyordered system. When there there are more than one order indices and fuzzy order rules, the proposed method provides one order index by mixing them.

• 응용통계연구
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• 제24권6호
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• pp.1077-1094
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• 2011

마이크로어레이 유전자 발현 데이터인 yeast cdc15에 대해 시계열 데이터의 특성을 반영한 푸리에 계수를 이용한 검정통계량과 FDR 다중비교법을 이용하여 차별화된 유전자를 선별한 후 선별된 유전자들에 대해 모형기반 군집방법, K-평균법, PAM, SOM, 계층적 Ward 군집방법과 Fuzzy 군집방법을 실시하였다. 군집방법에 따른 특성을 알아보고 군집화 결과와 내부유효성 측도로 연결성 측도, Dunn 지수와 실루엣 값을 살펴본다. 또한 GO분석을 통한 생물학적 의미도 파악해본다.

• Journal of Electrical Engineering and Technology
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• 제11권1호
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• pp.47-54
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• 2016

Microgrids offer several reliability benefits, such as the improvement of load-point reliability and the opportunity for reliability-differentiated services. The primary goal of this work is to investigate the impacts of operating condition on the reliability index for microgrid system. It relies on a component failure rate model which quantifies the relationship between component failure rate and state variables. Some parameters involved are characterized by subjective uncertainty. Thus, fuzzy numbers are introduced to represent such parameters, and an optimization model based on Fuzzy Chance Constrained Programming (FCCP) is established for reliability index calculation. In addition, we present a hybrid algorithm which combines scenario enumeration and fuzzy simulation as a solution tool. The simulations in a microgrid test system show that reliability indices without considering operating condition can often prove to be optimistic. We also investigate two groups of situations, which include the different penetration levels of microsource and different confidence levels. The results support the necessity of considering operating condition for achieving accurate reliability evaluation.

• Industrial Engineering and Management Systems
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• 제4권2호
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• pp.176-183
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• 2005

The theory of signal detection has been applied to a wide range of practical situation for a long time, including sonar detection, air traffic control and so on. In general, in this theory, sensitivity parametric index d' and bias parametric index $\beta$ are used to evaluated the performance of vigilance. These indices use observer's response "hit" and "false alarm" to explain and evaluate vigilance, but not considering reaction time. However, the reaction time of detecting should be considered in measuring vigilance in some supervisory tasks such as unlimited monitoring tasks (e.g., supervisors in nuclear plant). There are some researchers have used the segments of reaction time to generate a pair of probabilities of hit and false alarm probabilities and plot the receiver operating characteristic curve. The purpose of this study was to develop a quantitative vigilance-measuring model by fuzzy sets, which combined the concepts of hit, false alarm and reaction time. The model extends two-values logic to multi-values logic by membership functions of fuzzy sets. A simulated experiment of monitoring task in nuclear plant was carried out. Results indicated that the new vigilance-measuring model is more efficient than traditional indices; the characteristics of vigilance would be realized more clearly in unlimited monitoring task.

• 한국지능시스템학회논문지
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• 제5권3호
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• pp.11-35
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• 1995

본 논문에서는 학습/적응능력을 갖는 퍼지제어시스템들이 여러가지 관점에서 고찰되었다. 먼저, 기존에 제안된 다양한 학습/적응 퍼지제어시스템들의 기본적인 구성요소들을 바탕으로하여 이러한 시스템들의 일반적인 구조를 제안하였다. 그리고 제안된 구조의 중요한 구성요소들을 중심으로 고찰기준을 설정하였다. 고찰기준으로는 퍼지제어기나 퍼지모델 등에 사용되는 퍼지추론시스템의 구조, 학습/적응에 사용되는 퍼지추론시스템의 조정계수와 제어성능 평가함수, 그리고 학습/적응알고리즘을 설정하였다. 다음으로, 이러한 고찰기준들을 바탕으로하여 학습/적응 퍼지제어시스템들을 분류하고 각각의 특징들을 고찰하였다. 마지막으로, 사용된 퍼지추론시스템들의 범용 함수근사화 성질에 대하여도 알아 보았다.

• 제어로봇시스템학회논문지
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• 제6권3호
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• pp.273-283
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• 2000

In this paper we suggest an optimal design method of Fuzzy-Neural Networks(FNN) model for complex and nonlinear systems. The FNNs use the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM(Hard C-Means) Clustering Algorithm to find initial parameters of the membership function. The parameters such as parameters of membership functions learning rates and momentum weighted value is proposed to achieve a sound balance between approximation and generalization abilities of the model. According to selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity (distribution of I/O data we show that it is available and effective to design and optimal FNN model structure with a mutual balance and dependency between approximation and generalization abilities. This methodology sheds light on the role and impact of different parameters of the model on its performance (especially the mapping and predicting capabilities of the rule based computing). To evaluate the performance of the proposed model we use the time series data for gas furnace the data of sewage treatment process and traffic route choice process.