• 전기학회논문지
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• 제59권5호
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• pp.981-989
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• 2010

In this study, we introduce a new architecture of fuzzy inference system. In the fuzzy inference system, we use Fuzzy C-Means clustering algorithm to form the premise part of the rules. The membership functions standing in the premise part of fuzzy rules do not assume any explicit functional forms, but for any input the resulting activation levels of such radial basis functions directly depend upon the distance between data points by means of the Fuzzy C-Means clustering. As the consequent part of fuzzy rules of the fuzzy inference system (being the local model representing input output relation in the corresponding sub-space), four types of polynomial are considered, namely constant, linear, quadratic and modified quadratic. This offers a significant level of design flexibility as each rule could come with a different type of the local model in its consequence. Either the Least Square Estimator (LSE) or the weighted Least Square Estimator (WLSE)-based learning is exploited to estimate the coefficients of the consequent polynomial of fuzzy rules. In fuzzy modeling, complexity and interpretability (or simplicity) as well as accuracy of the obtained model are essential design criteria. The performance of the fuzzy inference system is directly affected by some parameters such as e.g., the fuzzification coefficient used in the FCM, the number of rules(clusters) and the order of polynomial in the consequent part of the rules. Accordingly we can obtain preferred model structure through an adjustment of such parameters of the fuzzy inference system. Moreover the comparative experimental study between WLSE and LSE is analyzed according to the change of the number of clusters(rules) as well as polynomial type. The superiority of the proposed model is illustrated and also demonstrated with the use of Automobile Miles per Gallon(MPG), Boston housing called Machine Learning dataset, and Mackey-glass time series dataset.

• 전기학회논문지
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• 제66권4호
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• pp.692-700
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• 2017

In this paper, we propose the structural design of Interval Type-2 FCM based RBFNN. Proposed model consists of three modules such as condition, conclusion and inference parts. In the condition part, Interval Type-2 FCM clustering which is extended from FCM clustering is used. In the conclusion part, the parameter coefficients of the consequence part are estimated through LSE(Least Square Estimation) and WLSE(Weighted Least Square Estimation). In the inference part, final model outputs are acquired by fuzzy inference method from linear combination of both polynomial and activation level obtained through Interval Type-2 FCM and acquired activation level through Interval Type-2 FCM. Additionally, The several parameters for the proposed model are identified by using differential evolution. Final model outputs obtained through benchmark data are shown and also compared with other already studied models' performance. The proposed algorithm is performed by using Iris and Vehicle data for pattern classification. For the validation of regression problem modeling performance, modeling experiments are carried out by using MPG and Boston Housing data.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2005년도 추계학술대회 학술발표 논문집 제15권 제2호
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• pp.345-348
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• 2005

In this paper, we suggested an evaluation model for satisfaction of e-Learning. This model was composed decision of evaluation criteria, analysis of consciousness structure for evaluation factors using the Fuzzy Structural Modeling method, decision of weights for evaluation factors considering intersectional dependence relations and evaluation of satisfaction of e-Learning. The case study of this model was done for comparative analysis between teachers and students of e-Learning in high school.

• 기술경영경제학회:학술대회논문집
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• 기술경영경제학회 2001년도 제19회 하계학술발표회 논문집
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• pp.61-70
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• 2001

Cross impact relationships exist among technologies. The purpose of this study is to develop a Cross Impact-based Technology Valuation Model necessary for evaluating the value of interdependent technology. For this purpose, cross impact relationships among interdependent technologies within specific technological system are analyzed by using Fuzzy Structural Modeling(FSM) Method. The model developed in this study will be a useful means of strategic decision making for companies which transact technologies.

• International Journal of Control, Automation, and Systems
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• 제1권2호
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• pp.194-202
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• 2003

In this paper, we introduce a category of Multi-FNN (Fuzzy-Neural Networks) models, analyze the underlying architectures and propose a comprehensive identification framework. The proposed Multi-FNNs dwell on a concept of fuzzy rule-based FNNs based on HCM clustering and evolutionary fuzzy granulation, and exploit linear inference being treated as a generic inference mechanism. By this nature, this FNN model is geared toward capturing relationships between information granules known as fuzzy sets. The form of the information granules themselves (in particular their distribution and a type of membership function) becomes an important design feature of the FNN model contributing to its structural as well as parametric optimization. The identification environment uses clustering techniques (Hard C - Means, HCM) and exploits genetic optimization as a vehicle of global optimization. The global optimization is augmented by more refined gradient-based learning mechanisms such as standard back-propagation. The HCM algorithm, whose role is to carry out preprocessing of the process data for system modeling, is utilized to determine the structure of Multi-FNNs. The detailed parameters of the Multi-FNN (such as apexes of membership functions, learning rates and momentum coefficients) are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the proposed model, two numeric data sets are experimented with. One is the numerical data coming from a description of a certain nonlinear function and the other is NOx emission process data from a gas turbine power plant.

• 한국지능시스템학회논문지
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• 제24권3호
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• pp.337-342
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• 2014

국제해사기구(IMO)는 해상에서의 안전과 보안, 환경의 보호를 위해 선박의 출항에서 입항에 이르기까지 항해와 관련된 정보를 전자 적인 방법으로 조화롭게 공유 통합 표현 분석하기 위해 e-Navigation 이라는 전략적 추진체계를 수립, 채택, 실행을 진행하고 있다. 본 논문에서는 e-Navigation의 실효적 시행에 대비하여 국내에서 운영 중인 GICOMS에 요구되는 개선방안 및 추가 시스템을 식별하기 위한 구조모델을 수립하는데 목적이 있다. 이를 위해서 GICOMS의 문제점을 조사하고, 전문가그룹의 브레인스토밍(brainstorming) 과정을 통해 GICOMS 개선을 위한 9개의 추진과제를 도출하였다. 이들 과제에 대하여 퍼지구조모델(FSM, Fuzzy Structural Modeling)을 이용하여 의식구조를 분석하고 요소 간 상호관계를 파악할 수 있는 구조모델을 제시하였다. 그 결과 GICOMS 개선방안 으로 "GICOMS 고도화"가 최상위층레벨 요소로 파악되었고, 최하층레벨은 "정보생산체제 개선", "정보제공방안 개선", "GICOMS와 VTS 상호연동"과 "글로벌 안전협력 네트워크 구축"으로 나타났다. 중간층레벨은 "e-Navigation 대응체계 구축", "해양사고예방체계 강화", "VMS 기능 고도화"와 "이용 활성화"로 파악되었다. 특히 최종목표인 "GICOMS 고도화"는 "VMS 기능 고도화"와 "이용 활성 화" 항목으로부터 많은 영향을 받고 있음을 알 수 있었다. 본 연구에서 구축한 GICOMS 고도화 구조모델을 기반으로 최첨단 IT를 활용한 시설 장비 전문인력의 개선 및 보강과 해양종사자 중심의 이용자 전환 등의 고도화 방안을 제시하였다.

• 한국시뮬레이션학회논문지
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• 제4권1호
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• pp.45-57
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• 1995

본 연구는 불확실한 경영환경하에서 전략목표에 영향을 미치는 환경요인을 확인하고, 이를 다시 전략계획 시뮬레이션 모형에 체계적으로 반영하기 위한 새로운 전략계획 시뮬레이션 모형을 제안한다. 본 연구에서 제안하는 전략계획 시뮬레이션 모형은 (1) 환경요인 분석을 위하여 퍼지인식도(Fuzzy Cognitive Map)를 적용하고, (2) 경쟁관계를 체계적으로 반영하기 위하여 미분게임(Differential Game) 모형을 이용한다. 퍼지인식도는 특정 의사결정 문제에 있어서 관련된 여러 개념간의 인과관계를 해석하고 그를 통하여 해당 문제전체에 관한 효과적인 의사결정을 지원하는 소위 구조적 모형화(Structural Modeling)도구의 한 방법이다. 한편, 본 연구에서는 미분게임을 이용하여, 퍼지인식도에 의하여 확인된 환경요인을 변수로 감안하고, 아울러 경쟁관계를 수식화 하므로써 보다 체계적인 시뮬레이션이 가능하다. 제안된 전략계획 수립 시뮬레이션 모형을 동태적 광고모형(dynamic advertising model)에 적용하므로써 보다 효과적인 경영전략계획 수립이 가능함을 보였다.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1997년도 가을 학술발표회 논문집
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• pp.24-31
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• 1997

Recently, in Korea, demand for establishment of systematic risk assessment techniques for construction projects has increased, especially after the large construction failures occurred during construction such as New Haengju Bridge construction projects, subway construction projects, gas explosion accidents etc. Most of existing risk analysis modeling techniques such as Event Tree Analysis and Fault Tree Analysis may not be available for realistic risk assessment of construction projects because it is very complex and difficult to estimate occurrence frequency and failure probability precisely due to a lack of data related to the various risks inherent in construction projects like natural disasters, financial and economic risks, political risks, environmental risks as well as design and construction-related risks. Therefor the main objective of this paper is to suggest systematic probabilistic risk assessment model and demonstrate an approach for probabilistic risk assessment using advanced Event Tree Analysis introducing Fuzzy set theory concepts. It may be stated that the Fuzzy Event Tree AnaIysis may be very usefu1 for the systematic and rational risk assessment for real constructions problems because the approach is able to effectively deal with all the related construction risks in terms of the linguistic variables that incorporate systematically expert's experiences and subjective judgement.

• 한국지능시스템학회논문지
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• 제15권2호
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• pp.245-250
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• 2005

인터넷의 급격한 발전으로 교육환경$\cdot$방법에 대한 새로운 패러다임 창출요구가 증가하고 있으며 전통적인 교육산업도 교육의 전 분야에서 이론 활용한 e-teaming이 많은 분야에서 도입되었고, 빠른 속도로 그 영역이 확장되고 있다. 이러한 e-learning 확산 노력에 힘입어 그동안 e-learning의 학습자 만족도에 대한 연구도 많이 진행되어 왔지만 기업체를 대상으로 한 연구가 거의 대부분이었고 고등학교를 대상으로 한 연구는 거의 없는 실정이다. 따라서, 본 연구에서는 이러한 배경을 바탕으로 고등학생을 대상으로 한 e-learning 교육만족도 평가를 위한 모델을 제안하고, 제안한 모델을 대상으로 퍼지구조 모델링법을 이용하여 고등학생의 e-learning 교육 만족도에 관한 의식구조를 분석하였다. 또한, 의식구조분석의 결과가 고려된 평가모델을 구축하여 e-learning 교육 만족도를 평가하고, 민감도분석을 통하여 e-learning 교육만족도 향상 방안을 제시 하였다.

• 한국항해학회지
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• 제25권4호
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• pp.477-486
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• 2001

Although the ports are actually competing with various strategies, the definition and structural understanding of port competitive power are not known very much. Therefore this study has launched from this fact, and has the objective of obtaining the structural model of the competitive power, and understanding the components of the port competitive power. The following are the results of the study. First, the process began by abstracting the components that composed the port competitive power through recent research, and grouping it by the most core components using the KJ method. Also, by using the FSM(Fuzzy Structural Modeling) method to understand the structure of the grouped components, and the structural model of the port competitive power was able to obtain as the result. Second, when analyzing the obtained structural model, port expenses, main trunk location, port congestion and port facility came out to be the most important component groups, and especially port expenses was the most effective component that effected all the other components overall. Third, the component groups that were relatively less important, effected by most of the other components, and located on the top level of the structure model were the hinterland accessibility, port ownership, customs duties speed, and large ship port entrance possibility etc. Fourth, the results of this study will be able to be used when establishing competing strategies for our country's ports by proposing the relatively important components with the port competitive rower considered.