• Science of Emotion and Sensibility
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• v.5 no.1
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• pp.17-24
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• 2002

This research was the first step to develop Expert System for Evaluation of Human Sensibility, where human sensibility can be inferred from objective physiological signals. The study aim was to develop an algorithm in which human arousal level can be judged using measured physiological signals. Fuzzy theory was applied for mathematical handling of the ambiguity related to evaluation of human sensibility, and the degree of belonging to a certain sensibility dimension was quantified by membership function through which the sensibility evaluation was able to be done. Determining membership function was achieved using results from a physiological signal database of arousal/relaxation that was generated from imagination. To induce one final result (arousal level) based on measuring the results of more than 2 physiological signals and the membership function of each physiological signal, Dempster-Shafer's Rule of Combination in Evidence was applied, through which the final arousal level was inferred.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.3
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• pp.188-193
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• 2000

This paper presents an approach to obtain a reduced neuro-fuzzy model for a plant. The Neuro-Fuzzy Network are compose of the Radial Basis Function Networks with Gausis membership and learned by using temporal back propagation. The dependency in rough set theory is used to eliminate rules. Dependency between the condition membership value of each rule in a model and the output of the plant can allow us to see how much contribution the rule is to identify the plant. While the reduced model maintains the same performance as the original one, the selection algorithm can minimize its complexity and redundancy of the structure.

• Journal of KIISE:Software and Applications
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• v.26 no.11
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• pp.1314-1323
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• 1999

컴퓨터의 사용이 일반화됨에 따라 데이타를 생성하고 수집하는 것이 용이해졌다. 이에 따라 데이타로부터 자동적으로 유용한 지식을 얻는 기술이 필요하게 되었다. 데이타 마이닝에서 얻어진 지식은 정확성과 이해성을 충족해야 한다. 본 논문에서는 데이타 마이닝을 위하여 퍼지 결정트리에 기반한 효율적인 퍼지 규칙을 생성하는 알고리즘을 제안한다. 퍼지 결정트리는 ID3와 C4.5의 이해성과 퍼지이론의 추론과 표현력을 결합한 방법이다. 특히, 퍼지 규칙은 속성 축에 평행하게 판단 경계선을 결정하는 방법으로는 어려운 속성 축에 평행하지 않는 경계선을 갖는 패턴을 효율적으로 분류한다. 제안된 알고리즘은 첫째, 각 속성 데이타의 히스토그램 분석을 통해 적절한 소속함수를 생성한다. 둘째, 주어진 소속함수를 바탕으로 ID3와 C4.5와 유사한 방법으로 퍼지 결정트리를 생성한다. 또한, 유전자 알고리즘을 이용하여 소속함수를 조율한다. IRIS 데이타, Wisconsin breast cancer 데이타, credit screening 데이타 등 벤치마크 데이타들에 대한 실험 결과 제안된 방법이 C4.5 방법을 포함한 다른 방법보다 성능과 규칙의 이해성에서 보다 효율적임을 보인다.Abstract With an extended use of computers, we can easily generate and collect data. There is a need to acquire useful knowledge from data automatically. In data mining the acquired knowledge needs to be both accurate and comprehensible. In this paper, we propose an efficient fuzzy rule generation algorithm based on fuzzy decision tree for data mining. We combine the comprehensibility of rules generated based on decision tree such as ID3 and C4.5 and the expressive power of fuzzy sets. Particularly, fuzzy rules allow us to effectively classify patterns of non-axis-parallel decision boundaries, which are difficult to do using attribute-based classification methods.In our algorithm we first determine an appropriate set of membership functions for each attribute of data using histogram analysis. Given a set of membership functions then we construct a fuzzy decision tree in a similar way to that of ID3 and C4.5. We also apply genetic algorithm to tune the initial set of membership functions. We have experimented our algorithm with several benchmark data sets including the IRIS data, the Wisconsin breast cancer data, and the credit screening data. The experiment results show that our method is more efficient in performance and comprehensibility of rules compared with other methods including C4.5.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.5
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• pp.853-862
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• 2003

With the data base subject of first grade paper test of information handling technician, We proposed special method of evaluating learning ability directivity to judge that student can understand the contents of each chapter exactly or not, using assigned function and fuzzy deduction in this thesis. Using fuzzy logic, the proposed method of evaluating learning ability is dividing the presenting frequency of setting questions for examination about the subject of database into three rank and we can define this as the important. We applied the fuzzy assigned rate about the number of times of studying through the important of studying and the fuzzy assigned rate about formative evaluation to each of nine fuzzy deduction theories and than evaluated comprehension rate of learning. With the fuzzy grade about learning comprehension of each chapter and assigned rate about the score of generalized evaluation; We applied these two thing to the deduction rule of fuzzy and made it as defuzzifier and finally evaluated learning. We made that the result of eventual evaluating learning is very useful for learners to diagnosis learned contents by themselves and also it can be great material to judge that learners can get the goal of learning or not synthetically.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.275-278
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• 2003

본 연구에서는 축산폐수공공처리장내에 설치된 Pilot-scale SBR(유효부피,20㎥)를 이용하여 sub-cycle의 폭기/무산소구간을 최적화하기 위하여 DO를 입력으로 하여 넓은 운전조건에서 적용될 수 있는 퍼지제어기를 개발하고, 또한 부하이상을 신속히 진단하여 유입부하량을 제어할 수 있는 퍼지 시스템 제어기를 개발하였다. DO값을 입력으로 한 퍼지제어기로서 안정성과 연속성에서 우수하였으나 시스템에 따라서 소속함수의 범위를 재조정해야 할 필요가 있다. DO미분값은 변화폭이 큰 지점을 검출함으로써 지연시간(lag time)의 DO값에 관계없이 적용할 수 있는 장점이 있다. 제어기의 적용성과 안정성을 높이기 위해서는 두 가지 제어인자를 동시에 고려할 필요가 있으며 퍼지 소속함수에 대한 입력으로서 DO값과 DO미분값을 적용하였다. 그 결과 폭기구간에서 매우 안정적이고 신속하게 폭기제어지점의 검출을 보여주고 있어 최적화된 제어가 가능함을 보여준다. 현장실험결과 지연시간에서의 DO가 높고 외란이 심한 경우에도 적용될 수 있음을 보여주었다.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.4
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• pp.109-119
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• 1999

Switched Reluctance Motor(SRM) have been expanding gradually their awlications in the variable speed drives due to their relatively low cost, simple and robust structure, controllability and high efficiency. In this paper neural network theory is used to detemrine fuzzy-neural network controller's membership ftmctions and fuzzy rules. In addition neural network emulator is used to emulate forward dynamics of SRM and to get error signal at fuzzy-neural controller output layer. Error signal is backpropagated through neural network emulator. The backpropagated error of emulator offers the path which reforms the fuzzy-neural network controller's mmbership ftmctions and fuzzy rules. 32bit Digital Signal Processor(TMS320C31) was used to achieve the high speed control and to realize the fuzzy-neural control algorithm. Simulation and experimental results show that in the case of load variation the proposed control rrethcd was superior to a conventional rrethod in the respect of speed response.sponse.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.1
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• pp.212-219
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• 2006

The important problem in designing a Fuzzy Logic Controller(FLC) is generation of fuzzy control rules and it is usually the case that they are given by human experts of the problem domain. However, it is difficult to find an well-trained expert to any given problem. In this paper, I describes an application of genetic algorithm, a well-known global search algorithm to automatic generation of fuzzy control rules for FLC design. Fuzzy rules are automatically generated by evolving initially given fuzzy rules and membership functions associated fuzzy linguistic terms. Using genetic algorithm efficient fuzzy rules can be generated without any prior knowledge about the domain problem. In addition expert knowledge can be easily incorporated into rule generation for performance enhancement. We experimented genetic algorithm with a non-trivial vehicle controling problem. Our experimental results showed that genetic algorithm is efficient for designing any complex control system and the resulting system is robust.

• The Journal of Korean Association of Computer Education
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• v.7 no.2
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• pp.77-84
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• 2004

In this thesis, We propose the self-directed learning and test-performing abilities assessment method to evaluate the learning and the test-performing abilities in which learners can not only control their own learning abilities for themselves, but also judge objectively learning and test-performing abilities. This method shows the membership degree of learning and test-performing abilities by using both the triangle-type membership function and the fuzzy logic. In addition, it gives the fuzzy grades to each item. The final membership degrees are calculated and the fuzzy grades are decided by the operation and composition of fuzzy relations on the membership degrees of learning and test-performing abilities. In this method, which is applicable to a writing subject for information searchers, learners are asked to analyse the membership degrees of the learning and test-performing abilities and the final fuzzy grades and to adjust a learning process for themselves.

• The Transactions of the Korea Information Processing Society
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• v.4 no.5
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• pp.1289-1296
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• 1997

To build up a knowledge-based system in an Artifical Inerligence System, selecting an appropriate set of rules is one of the key provlems. In this paper, we discuss a new method for exteacting fuzzy rules diredtly from fuzzy membdrchip function dat for pattern classifcation. The fuzzy rules with variable fuzzy recions are defined by sharing fuzzy space in fuzzy grid.Tehse rules are extracted form memberchop function. Them, optimal input vari-ables for the rules are determined using the number of extracted rules as a criterion. The method is compared with neural networks using Ishibuchi. Finally, in order to demonstrate the cffectiveness of the present method, simulation results are shown.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.2
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• pp.101-101
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• 1996

This paper presents the linearized fuzzy modeling technique of nonlinear dynamical system and the stability analysis of fuzzy control system. Firstly, the nonlinear system is partitionized by multiple linear fuzzy subcontrol systems based on fuzzy linguistic variables and fuzzy rules. Secondly, the disturbance adaptaion controllers which guarantee the global asymptotic stability of each fuzzy subsystem by an optimal feedback control law are designed and the stability analysis procedures of the total fuzzy control system using Lyapunov functions and eigenvalues are discussed in detail through a given illustrative example.