• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 1994년도 추계학술발표회 및 정기총회
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• pp.12-12
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• 1994

Simulation output knowledge analysis is one of problem-solving and/or knowledge adquistion process by investgating the system behavior under study through simulation . This paper describes an approach to simulation outputknowldege analysis using fuzzy neural network model. A fuzzy neral network model is designed with fuzzy setsand membership functions for variables of simulation model. The relationship between input parameters and output performances of simulation model is captured as system behavior knowlege in a fuzzy neural networkmodel by training examples form simulation exepreiments. Backpropagation learning algorithms is used to encode the knowledge. The knowledge is utilized to solve problem through simulation such as system performance prodiction and goal-directed analysis. For explicit knowledge acquisition, production rules are extracted from the implicit neural network knowledge. These rules may assit in explaining the simulation results and providing knowledge base for an expert system. This approach thus enablesboth symbolic and numeric reasoning to solve problem througth simulation . We applied this approach to the design problem of broadband communication network.

• 대한전기학회논문지:시스템및제어부문D
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• 제50권7호
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• pp.339-349
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• 2001

This paper suggests an optimal identification method for complex and nonlinear system modeling that is based on Fuzzy-Neural Networks(FNN). The proposed Hybrid Identification Algorithm is based on Yamakawa's FNN and uses the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. In this paper, the FNN modeling implements parameter identification using HCM algorithm and hybrid structure combined with two types of optimization theories for nonlinear systems. We use a HCM(Hard C-Means) clustering algorithm to find initial apexes of membership function. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are adjusted using hybrid algorithm. The proposed hybrid identification algorithm is carried out using both a genetic algorithm and the improved complex method. Also, an aggregated objective function(performance index) with weighting factor is introduced to achieve a sound balance between approximation and generalization abilities of the model. According to the 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 an optimal FNN model structure with mutual balance and dependency between approximation and generalization abilities. 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.

• Journal of Electrical Engineering and Technology
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• 제6권6호
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• pp.853-866
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• 2011

We propose a multi-objective space search algorithm (MSSA) and introduce the identification of fuzzy inference systems based on the MSSA and information granulation (IG). The MSSA is a multi-objective optimization algorithm whose search method is associated with the analysis of the solution space. The multi-objective mechanism of MSSA is realized using a non-dominated sorting-based multi-objective strategy. In the identification of the fuzzy inference system, the MSSA is exploited to carry out parametric optimization of the fuzzy model and to achieve its structural optimization. The granulation of information is attained using the C-Means clustering algorithm. The overall optimization of fuzzy inference systems comes in the form of two identification mechanisms: structure identification (such as the number of input variables to be used, a specific subset of input variables, the number of membership functions, and the polynomial type) and parameter identification (viz. the apexes of membership function). The structure identification is developed by the MSSA and C-Means, whereas the parameter identification is realized via the MSSA and least squares method. The evaluation of the performance of the proposed model was conducted using three representative numerical examples such as gas furnace, NOx emission process data, and Mackey-Glass time series. The proposed model was also compared with the quality of some "conventional" fuzzy models encountered in the literature.

• 한국항공우주학회지
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• 제35권11호
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• pp.983-989
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• 2007

위그선(WIG: Wing In Ground effect)은 해수면상의 저고도에서 운영되기 때문에 조류충돌에 쉽게 노출될 수 있으며 특히 리딩에지(Leading Edge)는 충돌가능성이 높기 때문에 조류충돌에 대한 구조적 안정성 검증과정이 반드시 필요하다. 이러한 항공기에 대한 조류충돌은 매우 복잡한 현상중의 하나로 다양하고 불확실한 파라메터들이 존재한다. 조류충돌에 대한 해석과정에 있어서 불확실성이 존재하는 파라메터들이 충돌 해석결과에 미치는 영향력을 분석하기 위해 퍼지이론을 적용한 유한요소 해석을 수행하였다. 불확실한 파라메터들이 갖는 조류충돌에 대한 영향력은 비선형 충돌해석 프로그램인 LS-DYNA 3D를 사용하여 해석하였으며 조류충돌 현상에 존재하는 다양하고 불확실한 파라메터들은 퍼지숫자(Fuzzy number)와 멤버십 함수(Membership function)로 표현하여 퍼지연산 및 변환기법(Transformation method)을 통해 해석과정에 적용하였다. 결론적으로 불확실성이 존재하는 파라메터들이 조류충돌 현상에 미치는 영향력을 퍼지 연산을 통해 수치적으로 평가하였다.

• 한국콘텐츠학회논문지
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• 제11권3호
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• pp.48-55
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• 2011

본 논문은 비선형 공정의 퍼지 모델을 동정하기 위해 전체 입력의 공간 분할 및 퍼지 추론 방법에 따른 퍼지 추론 시스템의 입출력 특성을 분석하며, 퍼지 모델의 입력 변수와 퍼지 입력 공간 분할 및 후반부 다항식 함수에 의한 구조 동정과 파라미터 동정을 통해 비선형 공정을 표현한다. 퍼지 규칙에서 전반부 파라미터의 동정에는 입출력 데이터의 최소 값과 최대 값을 이용하는 최소-최대 방법 및 입출력 데이터를 군집으로 형성하는 C-Means 클러스터링 알고리즘을 사용하여 입력 공간을 분할한다. 또한 전반부 멤버쉽 함수는 삼각형 멤버쉽 함수를 사용하여 입력 공간을 형성한다. 후반부 동정에서 퍼지 추론 방법은 간략 추론 및 선형 추론에 의해 시스템을 표현한다. 또한, 각 규칙의 후반부 파라미터들, 즉 후반부 다항식의 계수를 동정하기 위해 표준 최소자승법을 사용한다. 마지막으로, 비선형 공정으로는 널리 이용되는 가스로 데이터를 사용하며 이 공정에 대해 성능을 평가한다.

• Nuclear Engineering and Technology
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• 제28권5호
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• pp.467-481
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• 1996

An integrated framework of modeling the human operator cognitive behavior during nuclear power plant accident scenarios is presented. It incorporates both plant and operator models. The basic structure of the operator model is similar to that of existing cognitive models, however, this model differs from those existing ones largely in too aspects. First, using frame and membership function, the pattern matching behavior, which is identified as the dominant cognitive process of operators responding to an accident sequence, is explicitly implemented in this model. Second, the non-task-related human cognitive activities like effect of stress and cognitive biases such as confirmation bias and availability bias, are also considered. A computer code, OPEC is assembled to simulate this framework and is actually applied to an SGTR sequence, and the resultant simulated behaviors of operator are obtained.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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• pp.279-282
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• 2001

This paper presents an image contrast enhancement technique for improving the low contrast images using the improved IAFC(Integrated Adaptive Fuzzy Clustering) Model. The low pictorial information of a low contrast image is due to the vagueness or fuzziness of the multivalued levels of brightness rather than randomness. Fuzzy image processing has three main stages, namely, image fuzzification, modification of membership values, and image defuzzification. Using a new model of automatic crossover point selection, optimal crossover point is selected automatically. The problem of crossover point selection can be considered as the two-category classification problem. The improved MEC can classify the image into two classes with unsupervised teaming rule. The proposed method is applied to some experimental images with 256 gray levels and the results are compared with those of the histogram equalization technique. We utilized the index of fuzziness as a measure of image quality. The results show that the proposed method is better than the histogram equalization technique.

• 한국컴퓨터정보학회논문지
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• 제9권2호
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• pp.1-6
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• 2004

기존의 전자상거래시스템 검색 에이전트들은 고객이 상품 검색을 위해 사용할 수 있는 질의어에 대해 매우 제한적으로 동작해왔다. 본 논문은 고객이 전자상거래시스템에 접속하여 자신이 원하는 상품을 검색하기 위해 상품명을 제시했을 때, 해당 고객을 포함한 기존의 고객들의 프로파일 중 고객의 구매 행위에 결정적으로 영향을 미칠 수 있는 요소를 선행사건, 구매 성향과 관계된 요소를 후행사건으로 규정하여 고객에 대한 상품 적합도를 계산하고 적합도가 높은 상품 위주로 자동적으로 검색하여 고객에게 제시할 수 있는 퍼지-조건부 확률을 이용한 전자상거래 검색 에이전트를 제시한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 합동 추계학술대회 논문집 정보 및 제어부문
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• pp.420-423
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• 2001

In this paper, the Multi-FNNs(Fuzzy-Neural Networks) architecture is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNNs architecture uses simplified inference and linear inference as fuzzy inference method and error back propagation algorithm as learning rules. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNNs according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNNs model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model we use the time series data for gas furnace and the NOx emission process data of gas turbine power plant.

• 대한조선학회논문집
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• 제45권2호
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• pp.213-220
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• 2008

The aim of this paper is to build up the design model using ASMOD(Adaptive Spline Modeling of Observation Data) for the optimum scale of fleet, ship particulars and ship speed, etc. ASMOD, which define membership functions of fuzzy rule as B-spline basis function, represents a whole system as the sum of the sub-model. As it reduces the number of division of the space generated by the fuzzy set of input variables, it has a advantage of simplification to model structure and is efficient to represent the non-linear model.