• 정보처리학회논문지B
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• 제11B권6호
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• pp.717-722
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• 2004

본 논문은 가중 퍼지소속함수 기반 신경망(Neural Network with Weighted Fuzzy Membership Functions, NNWFM)을 이용하여 Wisconsin breast cancer의 예측을 수행하는 퍼지규칙을 추출하고 있다. NNWFM는 자기적응적(self adaptive)가중 퍼지소속함수를 가지고 주어진 입력 데이터로부터 학습하여 퍼지규칙을 생성하고 이론 기반으로 예측을 수행한다. 신경망 구조의 중간 부분인 하이퍼박스(hyperbox)들은 n개의 대, 중, 소의 가중 퍼지소속함수 집합으로 구성되며, 학습 후 각 집합은 퍼지집합의 bounded sum을 사용하여 다시 하나의 가중 퍼지소속함수로 합성된다. n개의 특징입력(feature input)은 학습된 모든 하이퍼박스에 연결되어 예측 작업을 수행한다. NNWFM으로 추출된 2개의 퍼지규칙은 99.41%의 예측 인식율을 가지며 이는 퍼지규칙의 수와 인식율에 있어 현재 발표된 논문의 결과보다 우수함을 보여준다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1993년도 춘계학술대회 논문집
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• pp.262-266
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• 1993

The development of COPS(Computer aided Operation Planning System) needs data mapping paradigm which provides intelligent determonation of cutting conditions from the requirements of process planning side. We proposed the idea of multi-level mapping by the combination of heuristics of domain experts and mathematical abstraction of cutting condition and requirements. Mathematical mathods for the generalization of heuristics were constructed by multi-layer perceptron. DBMS for determination of cutting conditions was constructed by classification and combination of best fitted models. Triangular fuzzy number was used to process the uncertainties in heuristics of experts.

• 전기학회논문지
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• 제57권11호
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• pp.2108-2116
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• 2008

The paper concerns Fuzzy C-Means clustering based Radial Basis Function neural networks (FCM-RBFNN) and the optimization of the network is carried out by means of Particle Swarm Optimization(PSO). FCM-RBFNN is the extended architecture of Radial Basis Function Neural Network(RBFNN). In the proposed network, the membership functions of the premise part of fuzzy rules do not assume any explicit functional forms such as Gaussian, ellipsoidal, triangular, etc., so its resulting fitness values directly rely on the computation of the relevant distance between data points by means of FCM. Also, as the consequent part of fuzzy rules extracted by the FCM - RBFNN model, the order of four types of polynomials can be considered such as constant, linear, quadratic and modified quadratic. Weighted Least Square Estimator(WLSE) are used to estimates the coefficients of polynomial. Since the performance of FCM-RBFNN is affected by some parameters of FCM-RBFNN such as a specific subset of input variables, fuzzification coefficient of FCM, the number of rules and the order of polynomials of consequent part of fuzzy rule, we need the structural as well as parametric optimization of the network. In this study, the PSO is exploited to carry out the structural as well as parametric optimization of FCM-RBFNN. Moreover The proposed model is demonstrated with the use of numerical example and gas furnace data set.

• 전기학회논문지
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• 제60권1호
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• pp.184-192
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• 2011

In this study, we introduce a identification methodology for FCM-based fuzzy model. The two underlying design mechanisms of such networks involve Fuzzy C-Means (FCM) clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on FCM clustering method for efficient processing of data and the optimization of model was carried out using PSO. The premise part of fuzzy rules does not construct as any fixed membership functions such as triangular, gaussian, ellipsoidal because we build up the premise part of fuzzy rules using FCM. As a result, the proposed model can lead to the compact architecture of network. In this study, as the consequence part of fuzzy rules, we are able to use four types of polynomials such as simplified, linear, quadratic, modified quadratic. In addition, a Weighted Least Square Estimation to estimate the coefficients of polynomials, which are the consequent parts of fuzzy model, can decouple each fuzzy rule from the other fuzzy rules. Therefore, a local learning capability and an interpretability of the proposed fuzzy model are improved. Also, the parameters of the proposed fuzzy model such as a fuzzification coefficient of FCM clustering, the number of clusters of FCM clustering, and the polynomial type of the consequent part of fuzzy rules are adjusted using PSO. The proposed model is illustrated with the use of Automobile Miles per Gallon(MPG) and Boston housing called Machine Learning dataset. A comparative analysis reveals that the proposed FCM-based fuzzy model exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.

• 대한토목학회논문집
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• 제33권3호
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• pp.917-926
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• 2013

본 연구는 로버스트 우선순위 결정을 위한 퍼지 다기준 의사결정기법의 타당성을 수자원 계획수립 문제에 적용하여 제시하였다. 즉 일반적인 다기준 의사결정 기법인 가중합계법, 계층화분석과정, 수정계층화분석과정, TOPSIS 방법과 퍼지가중합계법, 퍼지계층화분석과정, 퍼지수정계층화분석과정, 퍼지 TOPSIS 방법을 사용하여 결과를 비교하였다. 이때 사용된 각 평가기준별 자료는 동일하게 표준화되었으며 각 가중치도 동일한 방법으로 결정되었다. 분석결과 다기준 의사결정방법에 따라 조금씩 다른 순위가 도출되었으나, 퍼지 다기준 의사결정기법을 사용할 경우 사업들의 순위 변동성이 퍼지를 사용하지 않을 때보다 크지 않아 보다 일관된 순위를 유도하였다. 따라서 사업의 우선순위를 결정하는 문제에서 자료와 가중치의 불확실성을 고려할 수 있는 퍼지 다기준 의사결정기법을 활용해서 방법의 변화로 인한 순위의 변동성을 최소화해서 로버스트 순위를 결정하는 것이 보다 효과적이다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 D
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• pp.2519-2521
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• 2003

In this paper, we propose Self-Organizing Fuzzy Polynomial Neural Networks(SOFPNN) architecture for optimal model identification and discuss a comprehensive design methodology supporting its development. It is shown that this network exhibits a dynamic structure as the number of its layers as well as the number of nodes in each layer of the SOFPNN are not predetermined (as this is the case in a popular topology of a multilayer perceptron). As the form of the conclusion part of the rules, especially the regression polynomial uses several types of high-order polynomials such as linear, quadratic, and modified quadratic. As the premise part of the rules, both triangular and Gaussian-like membership function are studied and the number of the premise input variables used in the rules depends on that of the inputs of its node in each layer. We introduce two kinds of SOFPNN architectures, that is, the basic and modified one with both the generic and the advanced type. The superiority and effectiveness of the proposed SOFPNN architecture is demonstrated through nonlinear function numerical example.

• 한국지능시스템학회논문지
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• 제17권5호
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• pp.701-706
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• 2007

최근 고층건물의 방진시스템 및 자동차의 현가장치 분야에 MR 유체를 이용한 반능동 진동제어에 관한 연구가 활발히 진행되면서 MR 유체의 밸브모드 특성을 이용한 선형 MR 댐퍼가 널리 사용되고 있다. 그러나 전단모드를 이용한 회전형 MR 댐퍼에 관한 연구는 초기단계에 머물고 있다. 본 연구에서는, 주행로봇의 무한회전형 액츄에이터용 회전형 댐퍼의 진동제어에 관한 연구를 수행하였으며, 퍼지 로직을 기반으로 한 회전형 MR 댐퍼용 진동제어 알고리즘을 제안하였다. 퍼지제어기의 입출력 이득과 멤버쉽 함수의 모양은 유전자 알고리즘을 이용하여 조정하였다. 시뮬레이션 결과는 MR 댐퍼에 충격력이 가해질 때 퍼지 스카이-훅 제어의 제어성능을 보여준다.

• 한국지능시스템학회논문지
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• 제7권2호
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• pp.70-78
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• 1997

여러 가지 고급 제어 이론들에 관한 연구가 심도있게 진행되고 있음에도 불구하고 아직까지 산업현장에는 여러가지 변형된 형태의 PID 제어기가 널리 사용되고있다. 이는 PID 제어기 자체가 가진 제어 구조의 단순성, 효율성, 강건성, 그리고 제어 기술자들에 대한 친밀감 등에 기인한다. 또한 요즘 제어 분야에서는 퍼지 이론을 도입하는 연구가 활발히 진행되고 있다. 특히, 퍼지 이론을 사용해서 거의 모든 함수들을 근사화시킬 수 있다는 연구 결과들이 발표되면서 수학적으로 안정성 및 강건성을 명확히 증명하기에 다소 미흡하였던 퍼지 논리 제어에 관한 연구가 활기를 띠고 있다. 본 논문에서는 먼저 간단한 퍼지 제어기로부터 선형 PID 제어기를 유도한다. 그리고 나서 다소 일반적인 경우의 퍼지 제어기를 사용하여 산업 현장에서 가장 널리 사용되고있는 선형PID 제어기를 유도하여 결굴 PID 제어기는 퍼지 제어기의 일종에 불과함을 입증할 것이다.

• 한국안전학회지
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• 제36권2호
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• pp.49-57
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• 2021

In the case of military supplies, any potential failure and causes of failures must be considered. This study is aimed at examining the failure modes of a rotorcraft landing system to identify the priority items. Failure mode and effects analysis (FMEA) is applied to the rotorcraft landing system. In general, the FMEA is used to evaluate the reliability in engineering fields. Three elements, specifically, the severity, occurrence, and detectability are used to evaluate the failure modes. The risk priority number (RPN) can be obtained by multiplying the scores or the risk levels pertaining to severity, occurrence, and detectability. In this study, different weights of the three elements are considered for the RPN assessment to implement the FMEA. Furthermore, the FMEA is implemented using a fuzzy rule base, similarity aggregation model (SAM), and grey theory model (GTM) to perform a comparative analysis. The same input data are used for all models to enable a fair comparison. The FMEA is applied to military supplies by considering methodological issues. In general, the fuzzy theory is based on a hypothesis regarding the likelihood of the conversion of the crisp value to the fuzzy input. Fuzzy FMEA is the basic method to obtain the fuzzy RPN. The three elements of the FMEA are used as five linguistic terms. The membership functions as triangular fuzzy sets are the simplest models defined by the three elements. In addition, a fuzzy set is described using a membership function mapping the elements to the intervals 0 and 1. The fuzzy rule base is designed to identify the failure modes according to the expert knowledge. The IF-THEN criterion of the fuzzy rule base is formulated to convert a fuzzy input into a fuzzy output. The total number of rules is 125 in the fuzzy rule base. The SAM expresses the judgment corresponding to the individual experiences of the experts performing FMEA as weights. Implementing the SAM is of significance when operating fuzzy sets regarding the expert opinion and can confirm the concurrence of expert opinion. The GTM can perform defuzzification to obtain a crisp value from a fuzzy membership function and determine the priorities by considering the degree of relation and the form of a matrix and weights for the severity, occurrence, and detectability. The proposed models prioritize the failure modes of the rotorcraft landing system. The conventional FMEA and fuzzy rule base can set the same priorities. SAM and GTM can set different priorities with objectivity through weight setting.

• 전기학회논문지
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• 제57권3호
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• pp.466-472
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• 2008

The paper concerns Fuzzy C-Means clustering based fuzzy neural networks (FCM-FNN) and the optimization of the network is carried out by means of hierarchal fair competition-based parallel genetic algorithm (HFCPGA). FCM-FNN is the extended architecture of Radial Basis Function Neural Network (RBFNN). FCM algorithm is used to determine centers and widths of RBFs. In the proposed network, the membership functions of the premise part of fuzzy rules do not assume any explicit functional forms such as Gaussian, ellipsoidal, triangular, etc., so its resulting fitness values directly rely on the computation of the relevant distance between data points by means of FCM. Also, as the consequent part of fuzzy rules extracted by the FCM-FNN model, the order of four types of polynomials can be considered such as constant, linear, quadratic and modified quadratic. Since the performance of FCM-FNN is affected by some parameters of FCM-FNN such as a specific subset of input variables, fuzzification coefficient of FCM, the number of rules and the order of polynomials of consequent part of fuzzy rule, we need the structural as well as parametric optimization of the network. In this study, the HFCPGA which is a kind of multipopulation-based parallel genetic algorithms(PGA) is exploited to carry out the structural optimization of FCM-FNN. Moreover the HFCPGA is taken into consideration to avoid a premature convergence related to the optimization problems. The proposed model is demonstrated with the use of two representative numerical examples.