• 한국지능시스템학회논문지
• /
• 제10권3호
• /
• pp.194-202
• /
• 2000

본 논문에서는 여러 분야에서 널리 응용되고 있는 적응 뉴로-퍼지 시스템(ANFIS)에서의 효과적인 퍼지 규칙 생성 방법을 제안한다. 기존의 입력공간 그리드 분할을 이용한 ANFIS의 규칙 생성에 있어서는 얻어진 규칙의 수가 지수적으로 증가하는 단점이 있다. 이에, 본 연구에서는 조건부적인 FCM을 이용하여 입.출력 데이터이 특성을 잘 반영할 수 있는 클러스터를 구하고, 퍼지 균등화 방법을 적용하여 출력변수의 소속함수를 자동 생성하도록 하엿다. 이렇게 함으로서 적은 규칙 수를 갖으며서도 효율적인 퍼지 규칙을 얻을 수 있도록 하였다. 이들 방법의 유용함을 보이고자 트럭 후진제어와 Box-Jenkins의 가스로 데이터의 모델리에 적용하여 제안된 방법이 이전의 연구보다 좋은 결과를 보임을 알 수 있다.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 2003년도 춘계 학술대회 학술발표 논문집
• /
• pp.247-250
• /
• 2003

Due to the increasing use of very large databases, mining useful information and implicit knowledge from databases is evolving. However, most conventional data mining algorithms identify the relationship among features using binary values (TRUE/FALSE or 0/1) and find simple If-THEN rules at a single concept level. Therefore, implicit knowledge and causal relationships among features are commonly seen in real-world database and applications. In this paper, we thus introduce the mechanism of mining fuzzy association rules and constructing causal knowledge base form database. Acausal knowledge base construction algorithm based on Fuzzy Cognitive Map(FCM) and Srikant and Agrawal's association rule extraction method were proposed for extracting implicit causal knowledge from database. Fuzzy association rules are well suited for the thinking of human subjects and will help to increase the flexibility for supporting users in making decisions or designing the fuzzy systems. It integrates fuzzy set concept and causal knowledge-based data mining technologies to achieve this purpose. The proposed mechanism consists of three phases: First, adaptation of the fuzzy membership function to the database. Second, extraction of the fuzzy association rules using fuzzy input values. Third, building the causal knowledge base. A credit example is presented to illustrate a detailed process for finding the fuzzy association rules from a specified database, demonstration the effectiveness of the proposed algorithm.

• 정보처리학회논문지:소프트웨어 및 데이터공학
• /
• 제2권5호
• /
• pp.341-346
• /
• 2013

본 연구에서는 수정된 구조의 FMM 신경망으로부터 패턴 인식을 위한 규칙 추출 방법을 제안한다. 제안된 방법은 학습데이터에서 특징값에 대한 빈도 요소를 반영하는 하이퍼박스 정의를 기반으로 하는데, 이로부터 특징과 패턴클래스 간의 상호 연관도 요소를 정의 하였다. 이는 기존의 모델에서 사용되는 하이퍼박스 중첩테스트 및 축소(contraction) 기법을 사용하지 않아도 하이퍼박스의 중첩에 의한 분류의 모호성을 해결할 수 있게 한다. 본 연구에서는 패턴 클래스의 각 차원별로 퍼지 분할을 기반으로 하는 수정된 하이퍼박스 멤버쉽 함수와 이를 사용하는 학습방법을 제시한다. 제안된 기법으로부터 특정패턴의 분류를 위한 자극성(excitatory) 특징 및 억제성(inhibitory) 특징을 구분하고 이들 정보는 규칙 생성과정에 적용된다. 수화 인식에 관한 실험에 제안된 방법론을 적용함으로써 제안된 이론의 타당성을 실험적으로 고찰하였다.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 1996년도 추계학술대회 학술발표 논문집
• /
• pp.327-332
• /
• 1996

Rough Set theory suggested by Pawlak has a property that it can describe the degree of relation between condition and decision attributes of data which don't have linguistic information. In this paper, by using this ability of rough set theory, we define a occupancy degree which is a measure can represent a degree of relational quantity between condition and decision attributes of data table. We also propose a method that can find an optimal fuzzy rule table and membership functions of input and output variables from data without linguistic information and examine the validity of the method by modeling data generated by fuzzy rule.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제3권2호
• /
• pp.194-199
• /
• 2003

Data are an expression of the language or numerical values that show some features. And the information is extracted from data for the specific purposes. The knowledge is utilized as information to construct rules that recognize patterns or make a decision. Today, knowledge extraction and application of that are broadly accomplished for the easy comprehension and the performance improvement of systems in the several industrial fields. The knowledge extraction can be achieved by some steps that include the knowledge acquisition, expression, and implementation. Such extracted knowledge is drawn by rules with data mining techniques. Clustering (CL), input space partition (ISP), neuro-fuzzy (NF), neural network (NN), extension matrix (EM), etc. are employed for the knowledge expression based upon rules. In this paper, the various approaches of the knowledge extraction are surveyed and categorized by methodologies and applied industrial fields. Also, the trend and examples of each approaches are shown in the tables and graphes using the categories such as CL, ISP, NF, NN, EM, and so on.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제4권2호
• /
• pp.211-216
• /
• 2004

Fuzzy neural networks have been successfully applied to analyze/generate predictive rules for medical or diagnostic data. However, most approaches proposed so far have not considered the weights for the membership functions much. This paper presents a neural network with weighted fuzzy membership functions. In our approach, the membership functions can capture the concentrated and essential information that affects the classification of the input patterns. To verify the performance of the proposed model, well-known Iris data set is performed. According to the results, the weighted membership functions enhance the prediction accuracy. The architecture of the proposed neural network with weighted fuzzy membership functions and the details of experimental results for the data set is discussed in this paper.

• 통합자연과학논문집
• /
• 제11권4호
• /
• pp.167-183
• /
• 2018

Electroencephalogram (EEG) recording provides a new way to support human-machine communication. It gives us an opportunity to analyze the neuro-dynamics of human cognition. Machine learning is a powerful for the EEG classification. In addition, machine learning can compensate for high variability of EEG when analyzing data in real time. However, the optimal EEG electrode location must be prioritized in order to extract the most relevant features from brain wave data. In this paper, we propose an intelligent system model for the extraction of EEG data by training the optimal electrode location of EEG in a specific problem. The proposed system is basically a fuzzy system and uses a neural network structurally. The fuzzy clustering method is used to determine the optimal number of fuzzy rules using the features extracted from the EEG data. The parameters and weight values found in the process of determining the number of rules determined here must be tuned for optimization in the learning process. Genetic algorithms are used to obtain optimized parameters. We present useful results by using optimal rule numbers and non - symmetric membership function using EEG data for four movements with the right arm through various experiments.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2003년도 ICCAS
• /
• pp.1338-1343
• /
• 2003

Data is the expression of the language or numerical values that show some characteristics. And information is extracted from data for the specific purposes. The knowledge is utilized as information to construct rules that recognize patterns and make decisions. Today, knowledge extraction and application of the knowledge are broadly accomplished to improve the comprehension and to elevate the performance of systems in several industrial fields. The knowledge extraction could be achieved by some steps that include the knowledge acquisition, expression, and implementation. Such extracted knowledge can be drawn by rules. Clustering (CU, input space partition (ISP), neuro-fuzzy (NF), neural network (NN), extension matrix (EM), etc. are employed for expression the knowledge by rules. In this paper, the various approaches of the knowledge extraction are examined by categories that separate the methods by the applied industrial fields. Also, the several test data and the experimental results are compared and analysed based upon the applied techniques that include CL, ISP, NF, NN, EM, and so on.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 26-27 Oct. 1990
• /
• pp.246-251
• /
• 1990

A fuzzy control scheme has been proposed for a supercritical extraction process which has attracted much attention recently as a new separation technology. Based on the manual operation experience, three control pairs between manipulated and output variables are selected first and then seven membership functions are defined for control error and time rate of the error, respectively for each control pair, resulting in forty nine Fuzzy control rules. In addition to these, the membership functions are defined in two steps (coarse and fine) to enhance control performance. Fuzzy inference is performed using MAX-MTN composition rule and defuzzified control output is calculated based on center of gravity method. The prosed Fuzzy control scheme has been assessed through numerical simulation. As a result, the proposed scheme shows good control performance comparable with that by INA(inverse nyquist array) which usually requires complicated design procedure.

• 한국정보통신학회논문지
• /
• 제9권7호
• /
• pp.1582-1587
• /
• 2005

본 논문은 정수장에서 사용하는 응집제의 종류를 결정하기 위한 시스템 개발에 관한 내용이다. 정수장은 여러 단위 처리장으로 구성되며, 불순물을 제거하기 위하여 혼화지에서 응집제를 주입하여 침전을 시킨다. 현재까지 응집제 결정을 위해 Jar-test를 이용하는데, 이 방법은 사람의 주관적인 판단에 의존하므로 실험 오차가 발생할 수 있다. 특히 정수장의 자동화를 위한 시스템 개발에서 가장 큰 걸림돌로 작용하고 있다. 본 논문은 이러한 문제점을 해결하기 위하여 로드맵에 기초한 데이터마이닝 기법을 이용하여 응집제를 선택할 수 있는 제어기를 개발하였다. 제어 규칙은 클러스터링 기법으로 도출하였는데, 군집의 초기 값과 개수는 통계적 지수 값을 사용하여 결정하였다.