• 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.

• 한국정보처리학회논문지
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• 제5권11호
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• pp.2786-2796
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• 1998

대용량의 공간(spatial) 데이터베이스에서 사용자에게 관심있고 일반화된 지식을 추출하는 것은 지형 정보 시스템이나 지식 베이스 시스템의 개발에 중요한 기법중의 하나이다. 본 논문은 공간 데이터 마이닝에 널리 사용되는 일반화(generalization) 방법을 확장한 공간 데이터 마이닝 모듈에 공간 데이터를 추론할 수 있도록 구축된 규칙베이스(rulebase)를 통합한 공간데이터 마이닝 시스템을 제안한다. 이를 위한 전위기로서 공간 데이터 우선(spatial data dominated)과 비공간 데이터 우선(nonspatial data dominated) 마이닝을 병합한 방식과 다중 주제도(multiple thematic map)가 주어졌을 때의 공간 지식을 추출해 낼 수 있는 방식을 제안한다. 또한 후위기로서 공간 객체들간의 위상 관계(topological relationship)를 추론하기 위한 공간 규칙 베이스를 구축한다.

• 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.

• 대한전기학회논문지
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• 제39권5호
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• pp.527-537
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• 1990

This paper describes an intelligent control-based job scheduler, named ESIJOBS, for flexible manufacturing system. In order to construct rulebase of this system, traditional rules of job scheduling in FMS are examined and evaluated. This result and the repetitional simulations with graphic monitoring system are used to form the rulebase of ESIJOBS, which is composed of three parts:six part selection rules, four machine center selection rules, and twenty-one metarules. Appropriate scheduling rule sets are selected by this rulebase and manufacturing system status. The performances of all simulations are affected by random breakdowns of major FMS components during each simulation. Six criteria are used to evaluate the performance of each scheduling. The two modes of ESIJOBS are simulated and compared with combinational 24 rule-set simulations. In this comparison ESIJOBS dominated the other rule-set simulations and showed the most excellent performance particularly in three criteria.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1989년도 한국자동제어학술회의논문집; Seoul, Korea; 27-28 Oct. 1989
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• pp.286-289
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• 1989

The purpose of this paper is to design a job scheduling algorithm utilizing intelligent control technique. Rulebase is built through the evaluation of rule-set scheduling. 24 scheduling rule-sets and meta-rules are employed. An appropriate scheduling rule-set is selected based on this rulebase and current manufacturing system status. Six criteria have been used to evaluate the performance of scheduling. The performance of sheduling is dependent on random breakdown of the major FMS components during simulation.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 춘계정기학술대회
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• pp.111-135
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• 2001

• 대한전기학회:학술대회논문집
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• 대한전기학회 1991년도 하계학술대회 논문집
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• pp.358-362
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• 1991

This paper describes enhancements made on PROSET, an, expert system which can perform setting and coordination of protective relays used in the ultra-high voltage transmission systems. Enhancements include the friendly and convenient environment for rulebase management and system manipulation, expanded setting capability, and faster processing speed which have been achieved through adoption of the new rule representation, rule order independent IE, rulebase editor, local database generator, Interface to PSS/E fault program, RB expansion etc.

• 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.