• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.405-406
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• 2007

본 논문에서는 FCM 기반 퍼지 뉴럴네트워크 구조를 제안하고 진화 알고리즘을 이용한 FCM 기반 퍼지 뉴럴네트워크의 구조와 파라미터의 최적화 방법을 제시한다. 클러스터링 알고리즘은 퍼지 뉴럴 네트워크에서 멤버쉽함수의 중심점과 반경 등을 결정하는 학습에 일반적으로 사용된다. 제안된 FCM 기반 뉴럴 네트워크에서 멤버쉽함수는 가우시안, 삼각형 타입등의 정해진 형태를 사용하지 않고 데이터들 사이의 거리에 관계된 계산을 수행하는 FCM에 의해 결정된다. 후반부는 상수형, 선형, 2차식 등의 다양한 다항식 구조로 표현될 수 있으며 다항식의 계수는 LSE를 이용하여 결정한다. FCM 기반 퍼지 뉴럴 네트워크는 퍼지규칙의 수, 입력변수의 선택, 후반부 다항식의 차수, FCM의 퍼지화 계수의 결정은 성능에 많은 차이가 있으며 이러한 구조와 파라미터의 최적화가 요구된다. 본 논문에서는 유전자 알고리즘을 이용하여 FCM 기반 퍼지뉴럴네트워크의 구조에 관련된 입력변수의 수, 퍼지규칙의 수 그리고 후반부 다항식의 차수와 파라미터에 관련된 퍼지화 계수를 최적화 한다. 제안된 방법은 비선형 시스템의 모델링에 적용하여 성능을 분석하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.5
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• pp.51-60
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• 1998

There have been several recent studies concerning the stability of fuzzy control system and the synthesis of stabilizing fuzzy controllers. This paper reports on a related study nf the TS (Takagi-Sugeno) fuzzy systems, and it is shown that the controller synthesis problems for the nonlinear systems described by the TS fuzzy model can be reduced to convex problems involving LMIs (linear matrix ineclualities). After classifying the TS fuzzy systems into three families based on how diverse their input matrices are, different controller synthesis procedure is given for each of these families. A numerical example is presented to illustrate the synthesis procedures developed in this paper.

• Korean Journal of Construction Engineering and Management
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• v.5 no.2 s.18
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• pp.136-143
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• 2004

This study presents a risk management methodology using fuzzy theory for early construction stage and is focused on risk identification and risk analysis. This study identifies various risk factors associated with activities clearly construction stage, then establishes the Risk Breakdown Structure(RBS) by classifying the risks into the three groups; Common risks, risks for Earth works, and risks for Foundation works. The risk analysis method presented in this study is based on the RBS that has two levels such as upper level and lower level. The risk exposure of lower level risk factors is assessed by fuzzy inference. The weight of risks is estimated by fuzzy measure. Then, the estimated risk exposures and weights are aggregated to assess the risk exposure of upper level risks by Choquet fuzzy integral. The risk exposure of upper level risks determine the priority of risk factors in view of risk management. This study performs case study to validate the proposed method. The result of case study shows that the methodology suggested in this thesis would be utilized well in evaluating risk exposure.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.3
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• pp.81-88
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• 1996

Cluster analysis is based on partitioning a collection of data points into a number of clusters, where the data points in side a cluster have a certain degree of similarity and it is a fundamental process of data analysis. So, it has been playing an important role in solving many problems in pattern recognition and image processing. For these many clustering algorithms depending on distance criteria have been developed and fuzzy set theory has been introduced to reflect the description of real data, where boundaries might be fuzzy. If fuzzy cluster analysis is tomake a significant contribution to engineering applications, much more attention must be paid to fundamental questions of cluster validity problem which is how well it has identified the structure that is present in the data. Several validity functionals such as partition coefficient, claasification entropy and proportion exponent, have been used for measuring validity mathematically. But the issue of cluster validity involves complex aspects, it is difficult to measure it with one measuring function as the conventional study. In this paper, we propose four performance indices and the way to measure the quality of clustering formed by given learning strategy.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2853-2855
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• 1999

본 논문은 TS(Takagi-Sugeno) 퍼지 모델로 표현된 비선형 시스템의 최적 퍼지 제어에 관한 새로운 설계 방법론을 제시하며, 최적 TS 퍼지 제어기의 매개 변수들을 설정하는 문제가 선형 행렬 부등식 문제로 표현될 수 있음을 보인다.

• Journal of the Korean Institute of Intelligent Systems
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• v.3 no.3
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• pp.38-54
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• 1993

퍼지 제어기(FLC)는 Max-Min CRI 방법을 이용하여 추론하는 시스템이다. 그러나 이 방법은 주관적인 멤버쉽 함수의 결정, 오류 발생 가중치 전략, 비합리적인 추론 규칙들의 조합이라는 세가지 문제점 때문에 원하는 추론 결과와 실제 추론 결과 사이에 상당한 오류 영역을 발생시킨다. 본 논문에서는 이를 해결하기 위해 퍼지 이론에 신경 회로망의 학습 기능이 융합되어 지능적으로 작동하는 뉴로-퍼지 시스템(INFS)을 제안한다. INFS는 이상의 문제 해결 방안이 지식 획득 단계, 적응 조절단계를 통해 작동함으로써 임의의 입력에 대해서도 추론이 가능한 시스템이다. 제안된 INFS를직류 계열 모니터에 적용한 결과 신경 회로망을 사용하지 않았을때 보다 오류 영역을 상당히 줄여주었다. 또한 학습 시간을 고려해 볼 때, INFS에서 사용하는 추론 방법(NCRI 방법)이 지금까지 다른 방법에 비해 휠씬 효율적이었다.

• The Journal of Society for e-Business Studies
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• v.8 no.3
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• pp.53-67
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• 2003

The importance of web Mining is highlighted with growth of Electronic Commerce. Web Mining is the important field of subject for studying customer's purchasing trend in Electronic Commerce. This research defined customer's purchasing process as Fuzzy environment in Electronic Commerce. And it suggests new methodology that introduces Fuzzy theory based on current Web Mining methodology

• Journal of Korean Society of Transportation
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• v.15 no.4
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• pp.53-71
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• 1997

This paper presents a methodology for estimating average travel speed using volume and occupancy data from single magnetic loop detectors for signalized arterials. Three methods were developed and evaluated using field data: VPLUSKO method, fuzzy control method, and neural network method. While the VPLUSKO method is easy to apply, it results poor performances compared to other methods. The neural network method showed the best performances among the candidate methods. This method revealed the weakness in transferability, however. From limited cases of field test, it was concluded that the method of the fuzzy control application showed reasonable performance of estimation. It was also demonstrated that the fuzzy control method has the capability of transferability.

• Computational Structural Engineering
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• v.4 no.3
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• pp.107-115
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• 1991

A probabilistic-Fuzzy model for seismic hazard analysis is developed. The proposed model is able to reproduce both the randomness and the imprecision in conjunction with earthquake occurrences. Results-of this research are (a) membership functions of both peak ground accelerations associated with a given probability of exceedance and probabilities of exceedance associated with a given peak ground acceleration, and (b) characteristic values of membership functions at each location of interest. The proposed probabilistic-fuzzy model for assessment of seismic hazard is successfully applied to the Wasatch Front Range in Utah in order to obtain the seismic maps for different annual probabilities of exceedance, different peak ground accelerations, and different time periods.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.3
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• pp.252-259
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• 2000

In this paper, we propose a Fuzzy Classifier System(FCS) to find a set of fuzzy rules which can carry out the edge detection of a image. The FCS is based on the fuzzy logic system combined with machine learning. Therefore the antecedent and consequent of a classifier in FCS are the same as those of a fuzzy rule. There are two different approaches, Michigan and Pittsburgh approaches, to acquire appropriate fuzzy rules by evolutionary computation. In this paper, we use the Michigan style in which a single fuzzy if-then rule is coded as an individual. Also the FCS employs the Genetic Algorithms to generate new rules and modify rules when performance of the system needs to be improved. The proposed method is evaluated by applying it to the edge detection of a gray-level image that is a pre-processing step of the computer vision. the differences of average gray-level of the each vertical/horizontal arrays of neighborhood pixels are represented into fuzzy sets, and then the center pixel is decided whether it is edge pixel or not using fuzzy if-then rules. We compare the resulting image with a conventional edge image obtained by the other edge detection method such as Sobel edge detection.