• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.1371-1376
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• 1990

The paper presents a method of constructing simple fuzzy control rules for the determination of stabilizing signals of automatic voltage regulator and governor, which are controllers of electric power systems. Fuzzy control rules are simplified by considering a coordinate transformation with the rotation angle .theta. on the phase plane, and by expanding the range of membership functions. Also, two rotation angles .theta. $_{1}$ and .theta. $_{2}$ are selected for the linearizable region and the nonlinear one of the system, respectively. Here, .theta. $_{1}$ is chosen by the pole assignment method, and .theta. $_{2}$ by a performance index. Fuzzy inference is applied to the connection of two rotation angles .theta. $_{1}$ and .theta. $_{1}$ by regarding the distance from the desired equilibrium point as a variable of condition parts. The control effect is demonstrated by an application of the proposed method to one-machine infinite-bus power system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2380-2382
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• 2000

Fuzzy system which are based on membership functions and rules, can control nonlinear, uncertian, complex system well. However, Fuzzy controller has problems: It is difficult to design a stable for amateur. To update the then-part membership functions of the fuzzy controller can be designed using the error back-propagation algorithm to be minimized error. Then we could be optimized the system choosing a good performance index. The proposed fuzzy controller based on neural network is applied to control an inverted pendulum for demonstration of the robustness of proposed methodology.

• Industrial Engineering and Management Systems
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• 제3권1호
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• pp.85-91
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• 2004

The writing styles of TIME and Newsweek are analyzed using a specially developed linguistic program. These two news magazines were chosen because of their wide popularity. As for the results, it became obvious that both the frequency curve of words and that of characters have not changed for the past 60 years. Also, we have found that the frequency curves have some inflection points and that the genre of English writings can be identified by these points. After counting the percentage of required vocabulary for junior high school students and high school students in English writings, we can derive the relative difficulties of them using fuzzy reasoning. Fuzzy rules are constructed using features of the characteristic curves. We feel it would be a good guide index when selecting textbooks or supplementary readers.

• 한국항만경제학회지
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• 제31권1호
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• pp.85-110
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• 2015

본 논문에서는 아시아 컨테이너항만들 간의 클러스터링 추세를 분석하기 위해서 퍼지(평균지수변환)DEA모형과 교차효율성모형에 대해서 이론적으로 설명하고, 아시아 38개 컨테이너항만들의 12년간 자료를 4개의 투입요소(선석길이, 수심, 총면적, 크레인 수), 1개의 산출요소(컨테이너화물처리량)를 이용하여 국내항만(부산, 인천, 광양항)들이 어떤 항만들과 클러스터링 해야만 하는지에 대한 측정방법을 실증적으로 보여 주고 분석하였다. 실증분석의 주요한 결과는 다음과 같다. 첫째, 퍼지(평균지수변환)DEA모형에 의한 클러스터링 추세분석에서 국내항만들은 클러스터링을 통해서 효율성을 증대[부산항(56.29%), 인천항(57.96%), 광양항(66.80%)]시 킬 수 있는 것으로 나타났다. 둘째, 원자료를 이용한 교차효율성 모형을 이용한 클러스터링분석에서는 부산항(홍콩, 코오베, 마닐라, 싱가포르, 카오슝, 림찬방, 방콕항), 인천항(아카바, 담만, 카라치, 모하메드 빈 오아심, 다바오), 광양항(담만, 요코하마, 나고야, 킬롱, 카오슝, 방콕항)과 각각 클러스터링을 해야만 하는 것으로 나타났다. 셋째, 퍼지(평균지수변환)DEA모형에 교차효율성 모형을 접목시킨 모형에서는 부산항은 71.38%, 인천항은 103.89%, 광양항은 168.55% 증가가 이루어 졌다. 넷째, 효율성 순위를 검정한 윌콕슨부호순위검정에서는, 세 가지 모형사이의 효율성 순위에 대해서는 약 66%-67% 수준에서 순위에 차이가 없는 것으로 나타났다. 본 논문이 갖는 정책적인 함의는 첫째, 항만정책입안자들이 본 연구에서 사용한 두 가지 모형과 접목시킨 모형을 항만의 클러스터링 정책에 도입하여 해당항만이 발전할 수 있는 전략을 수립하고 이행해 나가야만 한다는 점이다. 둘째, 본 논문의 실증분석결과 국내항만들의 참조항만, 클러스터링항만들로서 나타난 아시아항만들에 대하여, 그들 항만들의 항만개발, 운영에 대한 내용을 정밀하게 분석하고 도입하여 실시하는 것이 필요하다.

• 한국지능시스템학회논문지
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• 제5권2호
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• pp.86-96
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• 1995

본 논문은 퍼지이론을 이용한 고전적인 역학문제의 확장에 관한 연구이다. 자중 및 휨강성 등 불확실한 매체를 갖는 기둥의 좌굴하중을 지배하는 미분방정식을 유도하였다. 수치해석예에서는 일단은 전형적인 자유, 회전, 고정단이고, 타단은 애매한 상수로 정의되는 회전스프링으로 지지된 기둥을 택하였다. 퍼지함수의 연산을 위하여 vertex method를 이용하였으며, 지배미분방정식의 수치적분과좌굴하중 결정을 위해 Runge-Kutta method와 행렬값탐사법을 각각 이용하였다. 좌굴하중의 소속함수를 산출하였으며,애매성의 흐름을 정량적으로 판단하기 위하여 퍼지지수를 정의 하였다. 지배인자의 애매성 변화에 따른 좌굴하중의 퍼지지수 변화를 분석하였으며, 단부 조건에 따른 감도를 고찰하였다.

• 한국지능시스템학회논문지
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• 제20권1호
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• pp.15-20
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• 2010

본 논문은 대규모 연속시간 T-S (Takagi-Sugeno) 퍼지 시스템의 고장검출을 위한 관측기 설계 문제를 논의한다. 관측기의 출력신호로부터 고장 여부를 판단하기 위해서 관측기는 고장신호에 가능한 민감해야하며 이를 위해 $\mathfrak{H}_-$ 성능지수를 도입한다. 설계 조건은 선형행렬부등식으로 표현되며 수치적 예제로부터 그 효용성을 확인한다.

• 산업경영시스템학회지
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• 제38권1호
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• pp.162-169
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• 2015

In this research, technology innovation capability evaluation model for small and medium enterprises was developed. To develop technology innovation capability evaluation model, two analytic technic was used. First one is AHP (Analytic Hierarchy Process) to give weight to each main index. Second one is fuzzy set theory to represent ambiguous index to numerical value. Finally, technology innovation capability evaluation model was achieved in combination with the same weight to AHP analysis and fuzzy set theory. With these results, small and medium enterprises can know important point in terms of strengthening the innovation capability evaluation.

• 산경연구논집
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• 제13권10호
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• pp.1-8
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• 2022

Purpose: Support vector machines (SVMs) ensemble has been proposed to improve classification performance of Credit risk recently. However, currently used fusion strategies do not evaluate the importance degree of the output of individual component SVM classifier when combining the component predictions to the final decision. To deal with this problem, this paper designs a support vector machines (SVMs) ensemble method based on fuzzy integral, which aggregates the outputs of separate component SVMs with importance of each component SVM. Research design, data, and methodology: This paper designs a personal credit risk evaluation index system including 16 indicators and discusses a support vector machines (SVMs) ensemble method based on fuzzy integral for designing a credit risk assessment system to discriminate good creditors from bad ones. This paper randomly selects 1500 sample data of personal loan customers of a commercial bank in China 2015-2020 for simulation experiments. Results: By comparing the experimental result SVMs ensemble with the single SVM, the neural network ensemble, the proposed method outperforms the single SVM, and neural network ensemble in terms of classification accuracy. Conclusions: The results show that the method proposed in this paper has higher classification accuracy than other classification methods, which confirms the feasibility and effectiveness of this method.

• International Journal of Control, Automation, and Systems
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• 제1권1호
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• pp.101-110
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• 2003

This paper introduces an identification method for nonlinear models in the form of rule-based Fuzzy-Neural Networks (FNN). In this study, the development of the rule-based fuzzy neural networks focuses on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The FNN modeling and identification environment realizes parameter identification through synergistic usage of clustering techniques, genetic optimization and a complex search method. We use a HCM (Hard C-Means) clustering algorithm to determine initial apexes of the membership functions of the information granules used in this fuzzy model. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are then adjusted using the identification algorithm of a GA hybrid scheme. The proposed GA hybrid scheme effectively combines the GA with the improved com-plex method to guarantee both global optimization and local convergence. An aggregate objective function (performance index) with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. According to the selection and adjustment of the weighting factor of this objective function, we reveal how to design a model having sound approximation and generalization abilities. The proposed model is experimented with using several time series data (gas furnace, sewage treatment process, and NOx emission process data from gas turbine power plants).

• 전기학회논문지
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• 제58권1호
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• pp.173-180
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• 2009

It is difficult to predict non-stationary or chaotic time series which includes the drift and/or the non-linearity as well as uncertainty. To solve it, we propose an effective prediction method which adopts data preprocessing and multiple model TS fuzzy predictors combined with model selection mechanism. In data preprocessing procedure, the candidates of the optimal difference interval are determined based on the correlation analysis, and corresponding difference data sets are generated in order to use them as predictor input instead of the original ones because the difference data can stabilize the statistical characteristics of those time series and better reveals their implicit properties. Then, TS fuzzy predictors are constructed for multiple model bank, where k-means clustering algorithm is used for fuzzy partition of input space, and the least squares method is applied to parameter identification of fuzzy rules. Among the predictors in the model bank, the one which best minimizes the performance index is selected, and it is used for prediction thereafter. Finally, the error compensation procedure based on correlation analysis is added to improve the prediction accuracy. Some computer simulations are performed to verify the effectiveness of the proposed method.