• Journal of Intelligence and Information Systems
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• v.15 no.3
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• pp.151-165
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• 2009

Fuzzy neural network is an integrated model of artificial neural network and fuzzy system and it has been successfully applied in control and forecasting area. Recently ANFIS(Adaptive Network-based Fuzzy Inference System) has been noticed widely among various fuzzy neural network models because of its outstanding accuracy of control and forecasting area. We design a new classification model based on ANFIS and evaluate it in terms of classification accuracy. We identified ANFIS-based classification model has higher classification accuracy compared to existing classification model, C5.0 decision tree model by comparing their experimental results.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.730-736
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• 2011

In this paper, we designed the models for pattern classification which can reflect the latest trend in time series. It has been shown that fusion models based on statistical and AI methods are superior to traditional ones for the pattern classification model supporting decision making. Especially, the hit rates of pattern classification models combined with fuzzy theory are relatively increased. The statistical SVM models combined with fuzzy membership function, or the models combining neural network and FCM has shown good performance. BPN, PNN, FNN, FCM, SVM, FSVM, Decision Tree, Time Series Analysis, and Regression Analysis were used for pattern classification models in the experiments of this paper. The economical indices DB with time series properties of the financial market(Korea, KOSPI200 DB) and the electrocardiogram DB of arrhythmia patients in hospital emergencies(USA, MIT-BIH DB) were used for data base.

• Journal of Computing Science and Engineering
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• v.8 no.2
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• pp.87-93
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• 2014

Coronary artery disease (CAD) is currently a prevalent disease from which many people suffer. Early detection and treatment could reduce the risk of heart attack. Currently, the golden standard for the diagnosis of CAD is angiography, which is an invasive procedure. In this article, we propose an algorithm that uses data mining techniques, a fuzzy expert system, and the imperialist competitive algorithm (ICA), to make CAD diagnosis by a non-invasive procedure. The ICA is used to adjust the fuzzy membership functions. The proposed method has been evaluated with the Cleveland and Hungarian datasets. The advantage of this method, compared with others, is the interpretability. The accuracy of the proposed method is 94.92% by 11 rules, and the average length of 4. To compare the colonial competitive algorithm with other metaheuristic algorithms, the proposed method has been implemented with the particle swarm optimization (PSO) algorithm. The results indicate that the colonial competition algorithm is more efficient than the PSO algorithm.

• IE interfaces
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• v.16 no.spc
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• pp.123-126
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• 2003

In this paper, we use three cluster algorithms (K-means, Self-Organizing Map, and Fuzzy K-means) to find proper graded stock market brokerage commission rates based on the cumulative transactions on both stock exchange market and HTS (Home Trading System). Stock trading investors for both modes are classified in terms of the total transaction as well as the corresponding mode of investment, respectively. Empirical analysis results indicated that fuzzy K-means cluster analysis is the best fit for the segmentation of customers of both transaction modes in terms of robustness. We then propose the rules for three grouping of customers based on decision tree and apply different brokerage commission to be 0.4%, 0.45%, and 0.5% for exchange market while 0.06%, 0.1%, 0.18% for HTS.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.6
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• pp.554-560
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• 2006

In this paper, we propose the topological map building method about unknown environment using the ultrasonic sensors. An ultrasonic sensor inherently has the range error due to the specular reflection. To decrease this error, we estimate the obstacle states(position and velocity) using the local minimum sensor values and Jacobian. Estimated states are used to avoid the obstacles and build the topological map similar to the type that human being memorizes an environment. When a mobile robot is faced with three problems(comer way, cross way and dead end), it senses the movable directions using FIS(Fuzzy Inference System). Among these directions, it can select the target direction using binary decision tree(Turn Side Selector). Proposed algorithm has been verified with three simulations and three implementations.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.5
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• pp.700-705
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• 2008

In this paper, a model to evaluate the sustainability management for small and middle enterprises was suggested. Also, the if-then rules and its decision tree for pattern analysis which is obtained by fuzzy ID3 from the data of sustainability management were shown. The suggested model can be used for the evaluation tool of competition increasement of enterprises. If the enterprise can recognize that the evaluation rule can be taken advantage of the sustainability management pattern analysis using fuzzy ID3, it is expected that they can use the rule effectively for self evaluation.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.1
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• pp.104-115
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• 2010

Development of the system which provides services using diverse sensors is expanding due to the widespread use of ubiquitous technology, and the research on the security technologies gaining attention to solve the vulnerability of ubiquitous environment's security. However, there are many instances in which flexible security services should be considered instead of strong only security function depending on the context. This paper used Fuzzy algorithm and MAUT to be aware of the diverse contexts and to propose context-aware security service which provides flexible security function according to the context.

• The KIPS Transactions:PartB
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• v.12B no.2 s.98
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• pp.203-208
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• 2005

We propose an algorithmic approach for handling linguistic values defined in the same linguistic variable. Using the proposed approach, we can explicitly capture the differences of individuals' subjectivity with respect to linguistic values defined in the same linguistic variable. The proposed approach can be employed as a useful tool for discovering hidden relationship among linguistic values defined in the same linguistic variable. Consequently, it provides a basis for improving the precision of knowledge acquisition in the development of fuzzy systems including fuzzy expert systems, fuzzy decision tree, fuzzy cognitive map, ok. In this paper, we apply the proposed approach to a collective linguistic assessment among multiple experts.

• Journal of KIISE:Software and Applications
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• v.28 no.1
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• pp.64-72
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• 2001

결정트리 생성은 일련의 특징값으로 기술된 사례들로부터 분류 지식을 추출하는 학습 방법중의 하나이다. 현장에서 수집되는 사례들은 관측 오류, 주관적인 판단, 불확실성 등으로 인해서 애매하게 주어지는 경우가 많다. 퍼지숫자나 구간값을 사용함으로써 이러한 애매한 데이타의 수치 속성은 쉽게 표현될 수 있다. 이 논문에서는 수치 속성은 보통값 뿐마아니라 퍼지숫자나 구간값을 갖을 수 있고, 비수치 속서은 보통값을 가지며, 데이터의 클래스는 확신도를 기자는 학습 데이터들로 부터, 분류 규칙을 마이닝하기 위한 퍼지 결정트리 생성 방법을 제안한다. 또한 제안한 방법에 의해 생성된 퍼지 결정트리를 사용하여, 새로운 데이터에 대한 클래스를 결정하는 추론 방법을 소개한다. 한편, 제안된 방법의 유용성을 보이기 위해 수행한 실험의 결과를 보인다.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.737-739
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• 1996

Abnormal conditions and disturbances in distribution system cause an immediate influence to the customers. Conventional detection schemes for the distribution abnormalities have been applied in limited extents mainly because of their low reliability. In this paper, we developed a disturbance identification system which monitors the load level after a transient, checks the harmonic behavior of the load, and finally makes decision on the cause of the disturbance. This system identifies and discriminates overcurrent faults, arcing ground faults, recloser activities, and foreign object or tree contacts. In the implementation of the identification system, we applied fuzzy logic to better represent some variables whose Quantities are expressed only in non-numerical terms.