• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 춘계학술대회 학술발표 논문집
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• pp.145-150
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• 1997

In this paper is studied a method of fuzzy logic control based on possibly inconsistent if-then rules representing uncertain knowledge or imprecise data. In most cases of practical applications adopting fuzzy if-then rule bases, inconsistent rules have been considered as ill-defined rules and, thus, not allowed to be in the same rule base. Note, however, that, in representing uncertain knowledge by using fuzzy if-then rules, the knowledge sometimes can not be represented in literally consistent if-then rules. In this regard, when it is hard to obtain consistent rule base, we propose the weighted rule base fuzzy logic control depending on output performance using neural network and we will derive the weight update algorithm. Computer simulations show the proposed method has good performance to deal with the inconsistent rule base fuzzy logic control. And we discuss the real application problems.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회/대한산업공학회 2003년도 춘계공동학술대회
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• pp.884-888
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• 2003

In this paper, we introduce the hybrid data mining mechanism based in association rule and fuzzy neural networks (FNN). Most of data mining mechanisms are depended in the association rule extraction algorithm. However, the basic association rule-based data mining has not the learning ability. In addition, sequential patterns of association rules could not represent the complicate fuzzy logic. To resolve these problems, we suggest the hybrid mechanism using association rule-based data mining, and fuzzy neural networks. Our hybrid data mining mechanism was consisted of four phases. First, we used general association rule mining mechanism to develop the initial rule-base. Then, in the second phase, we used the fuzzy neural networks to learn the past historical patterns embedded in the database. Third, fuzzy rule extraction algorithm was used to extract the implicit knowledge from the FNN. Fourth, we combine the association knowledge base and fuzzy rules. Our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic.

• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 2000년도 추계공동학술대회논문집
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• pp.375-389
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• 2000

Fuzzy logic ignores some information in the reasoning process. Neural networks are powerful tools for the pattern processing, but, not appropriate for the logical reasoning. To model human knowledge, besides pattern processing capability, the logical reasoning capability is equally important. Another new neural network called neural logic network is able to do the logical reasoning. Because the fuzzy inference is a fuzzy logical reasoning, we construct fuzzy inference network based on the neural logic network, extending the existing rule- inference. network. And the traditional propagation rule is modified. For the search strategies to find out the belief value of a conclusion in the fuzzy inference network, we conduct a simulation to evaluate the search costs for searching sequentially and searching by means of search priorities.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제4권3호
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• pp.361-365
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• 2004

In this paper, we present the modeling of time series data which are corrupted by noise via nonsingleton fuzzy logic system. Nonsingleton fuzzy logic system (NFLS) is useful in cases where the available data are corrupted by noise. NFLS is a fuzzy system whose inputs are modeled as fuzzy number. The abilities of NFLS to approximate arbitrary functions, and to effectively deal with noise and uncertainty, are used to analyze corrupted time series data. In the simulation results, we compare the results of the NFLS approach with the results of using only a traditional fuzzy logic system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권4호
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• pp.353-359
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• 2005

In this research, we propose a Unified Fuzzy rule-based knowledge Inference Systems (UFIS) to help the expert in cosmetic brand detection. Users' preferred cosmetic product detection is very important in the level of CRM. To this purpose, many corporations trying to develop an efficient data mining tool. In this study, we develop a prototype fuzzy rule detection and inference system. The framework used in this development is mainly based on two different mechanisms such as fuzzy rule extraction and RDB (Relational DB)-based fuzzy rule inference. First, fuzzy clustering and fuzzy rule extraction deal with the presence of the knowledge in data base and its value is presented with a value between 0 -1. Second, RDB and SQL (Structured Query Language)-based fuzzy rule inference mechanism provide more flexibility in knowledge management than conventional non-fuzzy value-based KMS (Knowledge Management Systems).

• 한국정보전자통신기술학회논문지
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• 제5권3호
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• pp.158-163
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• 2012

본 논문에서 나이브 베이지안 알고리즘, 데이터 마이닝, Fuzzy logic을 이용하여 이상 공격과 오용 공격을 탐지하는 하이브리드 침입탐지시스템인 FHIDS(Fuzzy logic based Hybrid Intrusion Detection System)을 설계하였다. 본 논문에서 설계한 FHIDS의 NB-AAD(Naive Bayesian based Anomaly Attack Detection)기법은 나이브 베이지안 알고리즘을 이용해 이상 공격을 탐지하고, DM-MAD(Data Mining based Misuse Attack Detection)기법은 데이터 마이닝 알고리즘을 이용하여 패킷들의 연관 규칙을 분석하여 새로운 규칙기반 패턴을 생성하거나 변형된 규칙 기반 패턴을 추출함으로써, 새로운 공격이나 변형된 공격을 탐지한다. 그리고 FLD(Fuzzy Logic based Decision)은 NB-AAD과 DM-MAD의 결과를 이용하여 정상인지 공격인지를 판별한다. 즉, FHIDS는 이상과 오용공격을 탐지 가능하며 False Positive 비율을 감소시키고, 변형 공격 탐지율을 개선한 하이브리드 공격탐지시스템이다.

• 한국멀티미디어학회논문지
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• 제18권3호
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• pp.387-400
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• 2015

A fuzzy logic based mobile robot navigation system was developed to improve the driving ability without trapping inside obstacles in complex terrains, which is one of the most concerns in robot navigation in unknown terrains. The navigation system utilizes the data from ultrasonic sensors to recognize the distances from obstacles and the position information from a GPS sensor. The fuzzy navigation system has two groups of behavior rules, and the robot chooses one of them based on the information from sensors while navigating for the targets. In plain terrains the robot with the proposed algorithm uses one rule group consisting of behavior rules for avoiding obstacle, target steering, and following edge of obstacle. Once trap is detected the robot uses the other rule group consisting of behavior rules strengthened for following edge of obstacle. The output signals from navigation system control the speed of two wheels of the robot through the fuzzy logic data process. The test was conducted in the Matlab based mobile robot simulator developed in this study, and the results show that escaping ability from obstacle is improved.

• 한국정보처리학회논문지
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• 제3권5호
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• pp.1138-1146
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• 1996

퍼지 논리는 추론과정에서 일부의 정보가 무시되어 적절하지 못한 추론 결과를 초래할 수 있다. 한편, 신경망은 패턴 처리에는 적합하지만 인간의 지식을 모델링하기 위해서 필요한 논리적인 추론에는 부적합하다. 그러나 신경망의 변형인 신경논리망 (neural logic network)을 이용하면 논리적인 추론이 가능하다. 따라서 본 논문에서는 기존의 신경논리망을 기반으로 하는 추론 네트워크를 확장하여 퍼지추론 네트워크(fuzzy inference network)를 구성한다. 그리고 기존의 추론 네트워크에서 사용되는 전파규칙 (propagation rule)을 보완하여 적용한다. 퍼지추론 네트워크 상에서 퍼지 규칙의 실행부에 해당하는 명제의 믿음값을 결정하기 위해서는 추론하고자 하는 명제에 연결된 노드들을 탐색해야 한다. 이를 위해서 연결된 모든 노드들의 링크를 따라 순차적인 탐색을 하는 경우와 링크에 부여된 우선순위에 의해 탐색을 하는 경우의 탐색비용에 대하여 실험을 통해 비교·평가한다.

• International Journal of Precision Engineering and Manufacturing
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• 제6권2호
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• pp.40-45
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• 2005

This paper has presented an application of an optimum grinding conditions based on the fuzzy logic. Fuzzy logic can handle vague and uncertain knowledge, and presents a scheme for integrating data with various kinds of grinding data. Especially, this research is capable of determining the grinding conditions taking into account some fuzzy membership function represented for trapezoidal form such as hardness and surface roughness of workpiece, material tensile strength and elongation, and requirement of grinding method. Larsen's fuzzy production method utilizing the fuzzy production rule can be applied on the establishment of grinding conditions, and also the output value obtained by the center of gravity method can effectively utilize the optimum grinding conditions.

• 한국농공학회논문집
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• 제65권4호
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• pp.33-43
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• 2023

In this study, it was developed and verified an application method of fuzzy-logic theory to the rating process of agricultural fill dam safety. A fuzzy-logic is very famous logical system when some decision making is made on the status of a lack of information. Three proxies were selected and configured membership functions (MFs) and these MFs were activated in the process of fuzzification procedures. Fuzzified vlaues were passed through the rule-based inference system, then fire strength could classified among cases of the rule-based inference system. To obtain final results, Mandani-type was adapted in the defuzzification process. As the results, it was shown the developed system can give a correct results that was compared with Matlab - fuzzy inference function. More ever it could perform the detailed analysis and improvement on the infrastructure safety rating process using classical diagnosis method.