• 대한기계학회논문집
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• 제17권8호
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• pp.1898-1909
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• 1993

In this paper, a convenient scheme for solving multi-objective optimization problems including fuzzy information in both objective functions and constraints is presented. At first, a multi-objective problem is converted into single objective problem based on the norm method, and a merbership function is constructed by selecting its type and providing the parameters defined by the norm method. Finally, this fuzzy programming problem is converted into an ordinary optimization problem which can be solved by usual nonlinear programming techniques. With this scheme, a designer can conveniently obtain pareto optimal solutions of a fuzzy system only by providing some parameters corresponding to the importance of the objectiv functions. Proposed scheme is simple and efficient in treating multi-objective fuzzy systems compared with and method by with membership function value is provided interactively. To show the validity of the scheme, a simple 3-bar truss example and optimal cutting problem are solved, and the results show that the scheme is very useful and easy to treat multi-objective fuzzy systems.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 추계학술대회 논문집 학회본부 B
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• pp.824-826
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• 1999

In this paper, we design an Fuzzy-Neural Networks(FNN) by means of divisions of fuzzy input space with multi-input variables. Fuzzy input space of Yamakawa's FNN is divided by each separated input variable, but that of the proposed FNN is divided by mutually combined input variables. The membership functions of the proposed FNN use both triangular and gaussian membership types. The parameters such as apexes of membership functions, learning rates, momentum coefficients, weighting value, and slope are adjusted using genetic algorithms. Also, an aggregate objective function(performance index) with weighting value is utilized to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model, we use the data of sewage treatment process.

• Journal of information and communication convergence engineering
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• 제8권4호
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• pp.383-386
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• 2010

Pattern classification is one of the most important topics for machine learning research fields. However incomplete data appear frequently in real world problems and also show low learning rate in classification models. There have been many researches for handling such incomplete data, but most of the researches are focusing on training stages. In this paper, we proposed two classification methods for incomplete data using triangular shaped fuzzy membership functions. In the proposed methods, missing data in incomplete feature vectors are inferred, learned and applied to the proposed classifier using triangular shaped fuzzy membership functions. In the experiment, we verified that the proposed methods show higher classification rate than a conventional method.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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• pp.45-49
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• 1997

In this paper, we controlled a Rotary Inverted Pendulum System using Neuro-Fuzzy Controller(NFC). The inverted pendulum system is widely used as a typical example of an unstable nonlinear control system which is difficult to control. Fuzzy theory have been because membership functions and rules of a fuzzy controller are often given by experts or a fuzzy logic control system. This controller is a feedforward multilayered network which integrates the basic elements and functions of a tradtional fuzzy logic controller into a connectionist structure which has distributed learning abilities. Such NFC can be constructed from training examples by learning rule, and the structure can be trained to develop fuzzy logic rules and find optimal input/output membership functions. Using this controller, we presented the results that controlled a Rotary Inverted Pendulum System and the associated algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.93.1-93
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• 2001

An evolutionary approach for the design of Fuzzy Logic Systems(FLSs) is proposed. Membership functions(MFs) in Takagi-Sugeno type fuzzy logic system is optimized through evolutionary process. Output singleton values are obtained through pseudo-inverse method. The proposed technique is unique for that, to prevent overfilling phenomenon, limited-level RBF membership functions are used and the new fitness function is invented. To show the effectiveness of the proposed method, some simulations results on model identification are given.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.556-561
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• 1998

This paper deals with our proposed fuzzy inference method, in which the fuzzy relation is represented by the membership functions of the antecedent and consequent parts, it is not used any fuzzy composition. The strong point of this method is that the membership function of an inferred conclusion has a simple shape and thus its meaning can be interpreted easily. Firstly, the proposed method is explained, and then it is applied to fuzzy modeling of distributed data.

• 한국지능시스템학회논문지
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• 제8권6호
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• pp.78-84
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• 1998

본 논문에서는 유전 알고리즘을 사용하는 퍼지 교통 제어기를 제안한다. 일반적인 퍼지 교통 제어기들은 사람에 의해 생성된 소속함수들을 사용한다. 그러나 이 방식은 퍼지 제어기를 설계하는데 최적의 해를 보장하지 못한다. 유전 알고리즘은 휴리스틱적인 특정 영역의 지식을 필요로 하는 최적화 문제의 좋은 해결 방법이다. 좋은 성능을 보이는 퍼지 소속함수를 찾기 위해서 적합도 함수가 정의되어야 한다. 그러나 교통 제어에서 적합도 함수를 수치 표현으로 정의하는 것은 쉽지 않다. 따라서 본 논문에서는 교통 시뮬레이터에 의해 얻어지는 성능척도로써 해의 적합도를 결정하는 시뮬레이션 접근법을 사용한다. 제안된 방법은 기존의 퍼지 제어기들에 비하여 우수한 성능을 보여준다.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2006년도 춘계종합학술대회
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• pp.788-791
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• 2006

많은 제어분야에서 비선형성이 강하고 명확하지 않는 장치를 효과적으로 제어하기 위해 1973년 Mamdani가 퍼지이론을 스팀-엔진제어에 성공적으로 적용한 이후, 퍼지 이론이 이러한 분야에서 효과적으로 응용되고 있다. Nguyen과 Widrow에 의해 최초로 제안된 Fuzzy truck backer-upper problem은 퍼지 제어 이론을 바탕으로 자동차를 제어하는 대표적인 비선형 제어 문제이다. 본 논문에서는 주변 환경에 적응적으로 트럭의 방향과 속도를 제어하기 위해서 방향 제어뿐만 아니라 속도 제어를 포함하는 퍼지 멤버쉽 함수를 설계하여 실제적으로 적용 가능한 적응적 퍼지 트럭 제어 시스템을 제안하고 Simulation을 통한 실험 및 검증을 수행한다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2005년도 춘계학술대회 학술발표 논문집 제15권 제1호
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• pp.297-300
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• 2005

In this study, we proposed genetically optimized self-organizing fuzzy polynomial neural network based on information granulation and evolutionary algorithm (gdSOFPNN), develop a comprehensive design methodology involving mechanisms of genetic optimization. The proposed gdSOFPNN gives rise to a structural Iy and parametrically optimized network through an optimal parameters design available within FPN (viz. the number of input variables, the order of the polynomial, input variables, the number of membership functions, and the apexes of membership function). Here, with the aid of the information granulation, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. The performance of the proposed gdSOFPNN is quantified through experimentation that exploits standard data already used in fuzzy modeling.

• 한국지능시스템학회논문지
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• 제11권8호
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• pp.720-725
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• 2001

비선형 미분방정식으로부터 TSK(Takagi-Sugeno-Kang) 퍼지모델을 유도한느 것은 퍼지 제어의 이론분야에서는 매우 중요한 문제이다. 본 논문에서는 off-equilibrium에서 상수항을 가지는 부분 미분 방정식을 배제시키는 방법을 제안한다. 이는 전건부의 언어적 표현이 삼각형 소속함수들을 가지는 기본적인 TSK 퍼지모델에서 체계적으로 유도되어진다. 그리고, 유도된 TSK 퍼지모델의 전건부 소속함수들은 GA(Genetic Algorithm)를 이용하여 최적화함으로써 실제 미분방적식에 근사화한다. 아울러 이상의 제안된 방법의 우수성을 모의실험을 통하여 검증한다.