• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.106-118
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• 1998

The study concerns a design procedure of rule-based systems. The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient from of "IF..., THEN..." statements, and exploits the theory of system optimization and fuzzy implication rules. The method for rule-based fuzzy modeling concerns the from of the conclusion part of the the rules that can be constant. Both triangular and Gaussian-like membership function are studied. The optimization hinges on an autotuning algorithm that covers as a modified constrained optimization method known as a complex method. The study introduces a weighted performance index (objective function) that helps achieve a sound balance between the quality of results produced for the training and testing set. This methodology sheds light on the role and impact of different parameters of the model on its performance. The study is illustrated with the aid of two representative numerical examples.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.3
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• pp.611-618
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• 2000

This paper presents a shape classification method of dynamic image based on adaptive fuzzy inference. It describes the design scheme of fuzzy inference algorithm which makes it suitable for low speed systems such as conveyor, uninhabited transportation. In the first Discrete Wavelet Transform(DWT) is utilized to extract the motion vector in a sequential images. This approach provides a mechanism to simple but robust information which is desirable when dealing with an unknown environment. By using feature parameters of moving object, fuzzy if - then rule which can be able to adapt the variation of circumstances is devised. Then applying the implication function, shape classification processes are performed. Experimental results are presented to testify the performance and applicability of the proposed algorithm.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.6
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• pp.415-424
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• 2004

In this paper, we introduce and investigate a new category of rule-based fuzzy inference system based on Information Granulation(IG). The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of “If..., then...” statements, and exploits the theory of system optimization and fuzzy implication rules. The form of the fuzzy rules comes with three types of fuzzy inferences: a simplified one that involves conclusions that are fixed numeric values, a linear one where the conclusion part is viewed as a linear function of inputs, and a regression polynomial one as the extended type of the linear one. By the nature of the rule-based fuzzy systems, these fuzzy models are geared toward capturing relationships between information granules. The form of the information granules themselves becomes an important design features of the fuzzy model. Information granulation with the aid of HCM(Hard C-Means) clustering algorithm hell)s determine the initial parameters of rule-based fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial function being used in the Premise and consequence Part of the fuzzy rules. And then the initial Parameters are tuned (adjusted) effectively with the aid of the improved complex method(ICM) and the standard least square method(LSM). In the sequel, the ICM and LSM lead to fine-tuning of the parameters of premise membership functions and consequent polynomial functions in the rules of fuzzy model. An aggregate objective function with a weighting factor is proposed in order to achieve a balance between performance of the fuzzy model. Numerical examples are included to evaluate the performance of the proposed model. They are also contrasted with the performance of the fuzzy models existing in the literature.

• Communications of the Korean Mathematical Society
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• v.18 no.4
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• pp.635-641
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• 2003

The notion of negative difference on a poset is introduced, and the interrelations between FI-algebras and posets with negative difference are discussed.

• Journal of Advanced Navigation Technology
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• v.10 no.3
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• pp.219-225
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• 2006

This paper presents a method of object classification based on discrete wavelet transform (DWT) and fuzzy inference(FI). It concentrated not only on the design of fuzzy inference algorithm which is suitable for low speed uninhabited transportation such as, conveyor but also on the minimize the number of fuzzy rule. In the preprocess of feature extracting, feature parameters are extracted by using characteristics of the coefficients matrix of DWT. Such feature parameters as area, perimeter and a/p ratio are used obtained from DWT coefficients blocks. Secondly, fuzzy if - then rules that can be able to adapt the variety of surroundings are developed. In order to verify the performance of proposed scheme, In the middle of fuzzy inference, the Mamdani's and the Larsen 's implication operators are utilized. Experimental results showed that proposed scheme can be applied to the variety of surroundings.

• Management & Information Systems Review
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• v.14
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• pp.79-99
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• 2004

Cyber education system service is in the field of software service which is highlighted after the latter half of 1990'. But the progress of this service is impeded by the lack of back office which contributes to the evaluation of learning achievement and the management of learning progress. This article points out the problem of current back office which is the most important in the cyber education system, and focuses on the new intelligent learning achievement evaluation module. First, we define the cause and effect between the learning stages using by fuzzy implication which is the important part of fuzzy theory. Next, we suggest the model which generates the results of the learning achievement evaluation. This model, suggested by this article, may contribute to the development of the cyber education system by improving the current on-line education service.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.4
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• pp.18-32
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• 1995

Fuzzy rules, developed by experts thus far, may be often inconsistent and incomplete. This paper proposes a new methodology for automatic generation of fuzzy rules which are nearly complete and not inconsistent. This is accomplished by simulating a knowledge gathering process of humans from control experiences. This method is simpler and more efficient than existing ones. It is shown through simulation that our method even generates better rules than those generated by experts, under fine tuned parameters.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.449-449
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• 2000

Many researchers have studied architectures for non-Neumann's computers because of escaping its bottleneck. To avoid the bottleneck, a neuron-based computer has been developed. The computer has only neurons and their connections, which are constructed of the learning. But still it has information processing facilities, and at the same time, it is like as a simplified brain to make inference; it is called "neuron-computer". No instructions are considered in any neural network usually; however, to complete complex processing on restricted computing resources, the processing must be reduced to primitive actions. Therefore, we introduce the instructions to the neuron-computer, in which the most important function is implications. There is an implication represented by binary-operators, but general implications for multi-value or fuzzy logics can't be done. Therefore, we need to use Lukasiewicz' operator at least. We investigated a neuron-computer having instructions for general implications. If we use the computer, the effective inferences base on multi-value logic is executed rapidly in a small logical unit.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.4
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• pp.41-55
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• 1995

In this paper, an optimal idenfication method using fuzzy-neural networks is proposed for modeling of nonlinear complex systems. The proposed fuzzy-neural modeling implements system structure and parameter identification using the intelligent schemes together wlth optimization theory, linguistic fuzzy implication rules, and neural networks(NNs) from input and output data of processes. Inference type for this fuzzy-neural modeling is presented as simplified inference. To obtain optimal model, the learning rates and momentum coefficients of fuzzy-neural networks(FNNs) are tuned automatically using improved modified complex method and modified learning algorithm. For the purpose of its application to nonlinear processes, data for route choice of traffic problems and those for activateti sluge process of sewage treatment system are used for the purpose of evaluating the performance of the proposed fuzzy-neural network modeling. The results show that the proposed method can produce the intelligence model with higher accuracy than other works achieved previously.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.2
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• pp.13-27
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• 1995

국내외 문헌을 조사해 볼때, 최적의 퍼지 함축을 선택하는 것이 퍼지 추론 및 퍼지 추론의 모든 응용 분야에서 근본적인 문제임을 알 수 있다. 그러나 많은 연구가들의 계속적인 연구에도 불구하고 개인적인 평가 기준과 사용되는 응용 모델에 따라 각기 다른 성능 평가가 이루어졌으므로 퍼지 함축의 선택 문제는 아직까지도 논란의 대상이 되고 있다. 최근 학습이론의 도입으로 퍼지 추론을 상당한 효과를 보았으나 퍼지 함축의 선택 문제와 관련된 연구는 전무하다. 따라서 본 논문에서는 유전자 알고리즘을 퍼지 추론에 적용했을 때의 퍼지 함축의 선택 문제를 고찰, 분석한다. 즉 유전자 알고리즘을 이용하여 퍼지 소속 함수를 조정함으로써 퍼지 추론 기관의 성능 향상뿐 아니라 폭 넓은 퍼지 함축의 선택이 가능하다.