• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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• pp.217-220
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• 2001

The fuzzy equalization method does not require the usual learning step for generating fuzzy rules. However it is heavily depend on the given input-output data set. So, we adapt an hierarchical scheme which sequentially optimizes the fuzzy inference system. Here, the parameters of fuzzy membership functions obtained from the fuzzy equalization are optimized by the genetic algorithm, and then they are also modified to increase the performance index using the linguistic hedge. Finally, we applied it to the Rice taste data and got better results than previous ones.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2004년도 추계학술대회 학술발표 논문집 제14권 제2호
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• pp.303-308
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• 2004

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes learning approach of fuzzy-neural network by immune algorithm. The proposed learning model is presented in an immune based fuzzy-neural network (FNN) form which can handle linguistic knowledge by immune algorithm. The learning algorithm of an immune based FNN is composed of two phases. The first phase used to find the initial membership functions of the fuzzy neural network model. In the second phase, a new immune algorithm based optimization is proposed for tuning of membership functions and structure of the proposed model.

• 한국안전학회지
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• 제12권2호
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• pp.70-79
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• 1997

This paper presents a study on the fuzzy linguistic variable based approach for safety assessment of human body in ELF electromagnetic field considering power system states. To cope with the demand in modern industry, the power system becomes larger in scale, higher in voltage. The advent of high voltage system has increased the relative importance of field effects. The analysis of ELF electromagnetic field based on Quasi-Static Method is introduced while the power system is included to model the expected and/or unexpected uncertainty caused by the load fluctuation and parameter changes. In order to analyze the power system, Monte Carlo simulation method and contingency analysis method are adopted in normal state and alert state, respectively. In the safety assessment of human body, the approach based on fuzzy linguistic variable is employed to overcome the shortcomings resulting from a crisp set concept. The suggested scheme is applied to a sample system(modified IEEE 14 bus system) to validate the usefulness.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.995-1000
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• 2003

This paper proposes the pedestrian navigation system which deals with subjective information. This system consists of the route setting part and the instruction generation part. The route setting part chooses the route with highest subjective satisfaction degree. The instruction generation part gives users the instructions based on the users' sensuous feeling of distance with linguistic expressions. Fuzzy measures and integrals are applied to the calculation of the satisfaction degree of the route which reflects the users' taste for routes. The instruction generation part has database of users' cognitive distance. Users' cognitive distances are expressed by fuzzy sets that correspond to linguistic terms. The system generates the instructions with linguistic terms which have the highest fitness value for the users' sensuous feeling of distance. This paper also performs subjective experiments in order to confirm the validity of the present system.

• 한국지능시스템학회논문지
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• 제1권1호
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• pp.9-25
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• 1991

This tutorial paper has been written for biologists, physicians or beginners in fuzzy sets theory and applications. This field is introduced in the framework of medical diagnosis problems. The paper describes and illustrates with practical examples, a general methodology of special interest in the processing of borderline cases, that allows a graded assignment of diagnoses to patients. A pattern of medical knowledge consists of a tableau with linguistic entries or of fuzzy propositions. Relationships between symptoms and diagnoses are interpreted as labels of fuzzy sets. It is shown how possibility measures (soft matching) can be used and combined to derive diagnoses after measurements on collected data. The concepts and methods are illustrated in a biomedical application on inflammatory protein variations. In the case of poor diagnostic classifications, it is introduced appropriate ponderations, acting on the characterizations of proteins, in order to decrease their relative influence. As a consequence, when pattern matching is achieved, the final ranking of inflammatory syndromes assigned to a given patient might change to better fit the actual classification. Defuzzification of results (i.e. diagnostic groups assigned to patients) is performed as a non fuzzy sets partition issued from a "separating power", and not as the center of gravity method commonly employed in fuzzy control. It is then introduced a model of fuzzy connectionist expert system, in which an artificial neural network is designed to build the knowledge base of an expert system, from training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the connections: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through MIN-MAX fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feed forward network is described and illustrated in the same biomedical domain as in the first part.

• 한국안전학회지
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• 제8권4호
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• pp.91-94
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• 1993

The main purpose of this study was to develop fuzzy models in order to decide system failure rate in industrial accident prevention. The purposed linguistic approach uses the Zadeh's concept of a linguistic variable with value which are not number. The problem of measurement Is the assignment of numbers to reprresnt properties of the involved events, object, or situation. Thus, in this study, part standard compatibility function was used.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.769-772
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• 1993

This paper presents and adaptive fuzzy controller using fuzzy neural networks(FNNs). The adaptive controller uses two FNNs. One FNN is used to identify a fuzzy model of controlled object. The other FNN is used as a fuzzy controller. The fuzzy controller is designed with the linguistic rules of the fuzzy model. The response of the designed control system is checked with a linguistic response analysis proposed by the authors. An adaptive tuning of the control rules of the FNN controller is made possible utilizing the fuzzy model. Simulations using nonlinear controlled objects were done to verify the proposed control system.

• 한국지능시스템학회논문지
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• 제9권6호
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• pp.577-582
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• 1999

Using a sole input variable simplifies the design process for the fuzzy logic controller(FLC). This is called single-input fuzzy logic controller(SFLC). However it is still deficient in the capability of adapting to the varying operating conditions. We here design a single-input adaptive fuzzy logic controller(AFLC) using a switching function of the sliding mode control. The AFLC can directly incorporate linguistic fuzzy control rules into the controller. Hence some parameters of the membership functions characterizing the linguistic terms of the fuzzy rules can be adjusted by an adaptive law. In the proposed AFLC center values of fuzzy sets are directly adjusted by a fuzzy logic system. We prove that 1) its closed-loop system is globally stable in the sense that all signals involved are bounded and 2)its tracking error converges to zero asymptotically. We perform computer simulation using a nonlinear plant.

• 한국마린엔지니어링학회:학술대회논문집
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• 한국마린엔지니어링학회 2001년도 춘계학술대회 논문집
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• pp.238-242
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• 2001

In this paper, we proposed an optimal identification method of the membership functions and the numbers of fuzzy rule base for the stabilization controller of the Thermal process control system by RVEGA. Although fuzzy logic controllers and expert systems have been successfully applied in many complex industrial process, they must rely on experts knowledges. So it is difficult in determination of the linguistic state space, definition of the membership functions of each linguistic term and the derivation of the control rules. To verify the validity of this RVEGA-based fuzzy controller, Thermal process control system, with strong nonlinear dynamics, was selected for application of this algorithm and compare with PI controller, and the empirically improved fuzzy controller.

• 전자공학회논문지SC
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• 제39권5호
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• pp.18-25
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• 2002

본 논문에서는 컴퓨터 연산을 통한 언어형 퍼지 제어 시스템의 안정도를 해석하는 새로운 방법을 제안한다. 제안하는 방법은 최근 각광을 받고 있는 선형행렬부등식을 이용한 방법이다. 기존의 수치적 방법과 비교할 때 본 논문에서 제안되는 방식의 특징은 좀더 완화된 방식으로 안정화 문제뿐 아니라 고정점 레귤레이션 문제에도 적용될 수 있는 특징을 가지고 있다. 끝으로 컴퓨터 모의 실험을 통하여 제안한 방법의 타당성을 확인한다.