• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.7C
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• pp.664-669
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• 2002

In this paper, we propose the fuzzy control algorithm for effective buffer control to connected traffic in ATM networks. The proposed Fuzzy control algorithm has two priorities and uses Fuzzy sets to search for dynamic thresholds. In this words, the difuzzification value controls the threshold in the buffer to according to traffic priority (low or high) using fuzzy set theory for traffic connected after reasoning. Performance analysis result: it was confirmed that with the proposed scheme, performance improves at cell loss rate, when compared with the existing PBS scheme.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.528-531
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• 2001

Finding a shortest path on a graph is a fundamental problem in the area of graph theory. In an application where we cannot exactly determine the weights of edges fuzzy weights can be used instead of crisp weights. and Type-2 fuzzy weight will be more suitable of this uncertainty varies under some conditions. In this paper, shortest path problem in type-1 fuzzy weighted graphs is extended for type 2 fuzzy weighted graphes. A solution is also given based on possibility theory and extension principle.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.367-370
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• 2005

본 논문에서는 정보 입자화와 유전자 알고리즘을 기반으로 최적 퍼지 다항식 뉴럴네트워크를 제안하고, 유전자 알고리즘을 사용하여 종합적인 설계방법을 개발한다. 제안된 모델은 기존의 진화론적 퍼지 다항식 뉴럴네트워크의 구조를 정보입자화를 통해 좀 더 빠르게 최적의 해공간에 접근시키는데 그 목적이 있다. 퍼지 관계기반 다항식 뉴럴네트워크는 퍼지 다항식 뉴론이 기초가 되어 가능한 구조적이고 요소적으로 모델의 성능을 향상 시켜준다. 퍼지 다항식 뉴런의 최적 구조를 위해 유전자 알고리즘을 이용하여 입력변수의 수와 후반부 다항식의 차수 입력변수 수에 따른 입력변수 그리고 멤버쉽 함수의 수를 동조한다. 여기서, 클러스터링의 하나의 방법인 HCM에 의해 퍼지 규칙 각각의 전반부와 후반부에 데이터 중심값을 이용하여 다항식함수의 파라미터값을 결정한다. 제안된 유전론적 퍼지 관계 다항식 뉴럴네트워크의 성능평가는 기존 퍼지 모델링에서 이용된 표준 데이터를 활용하여 평가한다.

• Proceedings of the Korea Multimedia Society Conference
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• 1998.04a
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• pp.394-399
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• 1998

본 논문에서는 Kohonen SOM을 이용한 인식 학습 알고리즘인 LVQ를 이용하여 퍼지 규칙의 수를 줄이는 방안을 제안하였다. 많은 훈련 패턴을 입력하게 되면 그에 따른 퍼지 규칙 수가 증가하게 되고, 많은 기억용량과 분류에 긴 시간을 필요로 하는 문제점 있어 퍼지 규칙의 수를 줄이고자 한다. 그러나 퍼지 규칙의 수가 줄어듦으로서 발생하는 성능의 하락을 최소화하기 위하여 초기 참조 패턴이 입력 데이터에 근접하도록 훈련 된 후에 퍼지 규칙을 생성하였다. 생성된 퍼지 규칙은 LVQ를 이용하여 인식되기 바로 전에 가중치 벡터를 이용하여 근접하는 값 이내에 있는 가중치 벡터 값을 합하여 같은 퍼지 규칙을 부여하여 생성하였다. 그 결과로 5$\times$8 숫자 Gray scale를 이용하여 전체 146개의 가중치 벡터가 15개의 아주 적은 수의 퍼지 규칙으로 생성되었다.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 1998.12a
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• pp.443-453
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• 1998

본 논문에서는 Petri-net 형태로 침입탐지 규칙을 구성한다. 이것은 실시간 침입탐지가 가능하고 지연공격과 다중공격을 방어할 수 있다. 그리고, Petri-net의 플레이스에 퍼지값을 적용한다. 이 값은 침입의 진행에 따라 변경되며 침입을 판정하는 기준이 된다. 또한, 변형공격에 대응할 수 있도록 한다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.8C
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• pp.1149-1158
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• 2004

In this thesis, we created a Fuzzy rule in a Fuzzy logic that are Fuzzy logic which is composed of linguistic rules and Fuzzy inference engine for effective traffic control in ATM networks. The parameters of the Fuzzy rules are adapted to minimize the given performance index in both cases. In other words, the difuzzification value controls the threshold in the buffer to arrival ratio to traffic priority (low or high) using fuzzy set theory for traffic connected after reasoning. Also, show experiment result about rule by MATLAB6.5 and on-line bulid-up to verify validity of created Fuzzy rule. As a result, we can verify that threshold value in buffer is efficiently controlled by the traffic arrival ratio.

• The Transactions of the Korea Information Processing Society
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• v.5 no.4
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• pp.951-958
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• 1998

In this paper, we propose a backward reasoning algorithm which can be utilized in the fuzzy Petri-net representation representing fuzzy production rules. The fuzzy Petri-net representation can be used to model a approximate reasoning system and implement a fuzzy inference engine. The proposed algorithm, which uses the proper belief evaluation functions according to fuzzy concepts in antecedentes and consequents of fuzzy production rules, is more closer to human intuition and reasoning than other methods. This algorithm generates the backward reasoning path from the goal to the initial nodes and evaluates the belief value of the goal node using belief evaluation functions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.11
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• pp.2289-2294
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• 2009

In this paper, we proposed a fuzzy controller for racing of a line trace vehicle. Sensor values are computed by statuses of line detecting sensors attached to the line trace vehicle and these sensor values are used for fuzzy inference rules of steering angle control to decide steering angle as output. The decided steering angle is also used for fuzzy inference rules of motor speed control to decide motor speed as output. We experimented and analyzed two proposed methods - one is fuzzy control of steering angle only and the other is fuzzy control of both steering angle and motor speed. In the experiment, we verified that the second proposed method was more efficient in racing speed.

• Journal of the Korean Society for information Management
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• v.10 no.1
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• pp.53-63
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• 1993

The conventional fuzzy set model has been criticized as a retrieval model because the MIN and MAX operators have the properties adverse to effective calculation of document values. Since the first introduction of fuzzy set theory a variety of fuzzy operators have been developed, which can replace the MIN and MAX operators. We analyze their behavioral aspects of generating document values, and propose the enhanced fuzzy set model based on a class of fuzzy operators called positively compensatory operators. We also show through performance experiments that the proposed fuzzy set model provides higher retrieval effectiveness.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.2
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• pp.7-13
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• 1999

This paper propose a fuzzy regression method using fuzzy neural networks when a membership value is attached to each input -output pair. First, an architecture of fuzzy neural networks with fuzzy weights and fuzzy biases is shown. Next, a cost function is defined using the fuzzy output from the fuzzy neural network and the corresponding target output with a membership value. A learning algorithm is derived from the cost function. The derived learning algorithm trains the fuzzy neural network so that the level set of the fuzzy output includes the target output. Last, the proposed method is applied to the quality evaluation problem of injection molding