• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권3호
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• pp.182-187
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• 2007

A fuzzy modeling method is proposed to build the dynamic model of a walking-beam reheating furnace from the recorded data. In the proposed method, the number of membership function on each variable is increased individually and the modeling accuracy is evaluated iteratively. When the modeling accuracy is satisfied, the membership functions on each variable are fixed and the structure of fuzzy model is determined. Because the training data is limited, in this process, as the number of membership function increase, it is highly possible that some rules are missing, i.e., no data in the training set corresponds to the consequent part of a missing rule. To complete the rulebase, the output of the model constructed at the previous step is used to generate the consequent part of the missing rules. Finally, in the real time application, a rolling update scheme to rulebase is introduced to compensate the change of system dynamics and fine tune the rulebase. The proposed method is verified by the application to the modeling of a reheating furnace.

• 한국지능시스템학회논문지
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• 제17권2호
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• pp.276-283
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• 2007

It has been shown that the structure identification of a neuro-fuzzy model improves their accuracy performances in a various modeling problems. In this paper, we claim that the structure identification of a neuro-fuzzy model can also reduce the degree of inconsistency of its fuzzy rulebase. Thus, the resulting neuro-fuzzy model serves as more like a structured knowledge representation scheme. For this, we briefly review a structure identification method of a neuro-fuzzy model and propose a systematic method to measure inconsistency of a fuzzy rulebase. The proposed method is applied to problems or fuzzy system reproduction and nonlinear system modeling in order to validate our claim.

• 제어로봇시스템학회논문지
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• 제9권10호
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• pp.782-788
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• 2003

Making deicing countermeasure plan against snow accretion on power line is a very complicated problem, which should take into account both the possibility of accidents due to snow accretion on power line and the stable operation of power system. As knowledge engineering can be a good solution to this field of problems, a prototype expert system to assist power system operators in forecasting snow accretion on power lines and making a list of all the feasible and effective deicing countermeasures has been developed. The system has been remodelled into a fuzzy expert system by adopting fuzzy rulebase and fuzzy inference method to systematically process the fuzziness included in the heuristic knowledges. Simulation results based on the past snow accretion accident data show that the proposed system is very promising.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 A
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• pp.35-37
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• 2001

The four fuzzy criteria to distinguish the internal fault from the inrush for the power transformer protection have been identified. They are based on the wave shape, terminal voltage, fundamental and second harmonic component of differential current. A systemetic way to determine the associated fuzzy membership function is also proposed.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1029-1032
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• 1993

The construction of the rulebase of a fuzzy controller is usually difficult because experts' knowledge is often hard to derive. To remedy such a problem, a number of self-learning schemes for rulebase formulations were proposed. One of the popular approaches is the reinforcement learning. Many successful examples employing such an idea were proposed and claimed to be with good results in the literature. The purpose of this paper is to discuss and make comparisons between some of the related work in order to provide a better picture regarding their performances. A numerical algorithm for the analysis of nonlinear as well as fuzzy dynamic systems, the Cell-to-Cell Mapping, is used. The analytical results reveals the true behavior of the learning schemes.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제1권1호
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• pp.75-80
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• 2001

Rule inconsistency is an important issue that is needed to be addressed while designing efficient and optimal fuzzy rule bases. Automatic generation of fuzzy rules from data sets, using machine learning techniques, can generate a significant number of redundant and inconsistent rules. In this study we have shown that it is possible to provide a systematic approach to understand the fuzzy rule inconsistency problem by using the proposed measure called the Commonality measure. Apart from introducing this measure, this paper describes an algorithm to optimize a fuzzy rule base using it. The optimization procedure performs elimination of redundant and/or inconsistent fuzzy rules from a rule base.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.710-717
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• 1993

In this paper, we focus upon the design and applications of adaptive fuzzy-neuro controllers. An intelligent control system is proposed by exploiting the merits of two paradigms, a fuzzy logic controller and a neural network, assuming that we can modify in real time the consequential parts of the rulebase with adaptive learning, and that initial fuzzy control rules are established in a temporarily stable region. We choose the structure of fuzzy hypercubes for the fuzzy controller, and utilize the Perceptron learning rule in order to update the fuzzy control rules on-line with the output error. And, the effectiveness and the robustness of this intelligent controller are shown with application of the proposed adaptive fuzzy-neuro controller to control of the cart-pole system.

• 한국지능시스템학회논문지
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• 제6권4호
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• pp.49-60
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• 1996

본 논문의 목적은 신경망을 이용한 퍼지 하이퍼큐브의 적응 학습 제어알고리듬의 개발이다. 퍼지 시스템 규칙베이스 후건부의 실시간적인 수정, 초기 퍼지 제어규칙의 일시적인 안정성을 가정하여 퍼지제어기와 신경망의 장점만을 살린 지능형 제어시스템의 설계방법을 제안하였다. 퍼지 제어기로는 실현 가능한 퍼지 하이퍼큐브의 구조를 선택하였고, 퍼셉트론 신경만의 학습법칙을 적용하여 출력오차로써 퍼지 제어기의 규칙을 실시간적으로 수정해 나가는 방법을 사용하였다. 결과적으로 적응 퍼지-뉴로 제어시스템을 Cart-Pole 제어에 응용함으로써 이러한 지능형 제어기의 유효성과 강인성을 보였다.

• 공학교육연구
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• 제3권1호
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• pp.21-26
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• 2000

월드 와이드 웹은 사이버 교육에 있어서 인터넷을 통한 새로운 기회를 제공한다. 웹은 다른 네트워크 기술과 결합하여 학습자에게 유용한 교육정보를 제공하는데 유용하다. 따라서, 본 논문의 목적은 인터넷상에서 퍼지 논리 제어기의 개념을 이해하기 위한 자바 애플릿을 개발하는 것이다. 개발된 자바 애플릿은 4개의 프레임(퍼지화기, 제어규칙, 추론 엔진, 비퍼지화기)으로 구성이 되어 있다. 데이터의 전송은 하나의 프레임에서 나머지 프레임으로 전송이 되도록 하여, 사용자가 쉽게 퍼지 논리 제어기의 수행과정을 관찰하고 이해할 수 있도록 하였다. 본 논문의 결과는 사이버 대학에서 사이버 강의의 능률을 향상시키는데 사용될 수 있다.

• 대한설비공학회:학술대회논문집
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• 대한설비공학회 2005년도 동계학술발표대회 논문집
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• pp.427-432
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• 2005

This study developed a program for detecting charged refrigerant amount in system air-conditioner. System air-conditioner is an air-conditioning system with multiple indoor units. Due to the complexity of the system, it is more difficult to detect the refrigerant amount charged in system air-conditioner than in a general single air-conditioner. Experiments were performed for 6 HP outdoor units with 3 indoor units in a psychrometric calorimeter. The experimental amount of charged refrigerant were ranged from 60% to 140% with 10% increasement. Fuzzy algorithm were emploeed for detecting the charged refrigerant amount in a system air-conditioner. The experimental data were used for curve fitting for general ranges for indoor and outdoor temperature conditions. membership function were determined for whole ranges of experimentally measured data and rulebase were defined for each amount of refrigerant charge. Developed program successfully predicted the measured data within 10% resolution range.