• International Journal of Fuzzy Logic and Intelligent Systems
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• 제2권3호
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• pp.210-214
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• 2002

Association rule mining is an exploratory learning task to discover some hidden dependency relationships among items in transaction data. Quantitative association rules denote association rules with both categorical and quantitative attributes. There have been several works on quantitative association rule mining such as the application of fuzzy techniques to quantitative association rule mining, the generalized association rule mining for quantitative association rules, and importance weight incorporation into association rule mining fer taking into account the users interest. This paper introduces a new method for generalized fuzzy quantitative association rule mining with importance weights. The method uses fuzzy concept hierarchies fer categorical attributes and generalization hierarchies of fuzzy linguistic terms fur quantitative attributes. It enables the users to flexibly perform the association rule mining by controlling the generalization levels for attributes and the importance weights f3r attributes.

• Korean Chemical Engineering Research
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• 제59권2호
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• pp.186-190
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• 2021

규제당국은 유해 물질 배출을 저감 하는 규제를 시행하면서, 규제 이행에 따른 다양한 활동의 편익/비용 비율로 그 타당성을 제시한다. 그리고 비율 산정과정에서 규제 관련 속성의 중요도 가중치를 반영한다. 그런데, 전문가들은 가중치 설문 응답에서 애매함을 보이고 있으며, 정확한 의사 결정을 위해서는 이를 제거해야 한다. 본 연구에서는 이를 위한 퍼지-계층화 방법론을 이용하여 타당성여부를 적용하고, 이를 2019년 11월 시행된 우리나라의 '배출 저감 계획'규제 타당성 평가에 적용하였다.

• Journal of the Korean Data and Information Science Society
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• 제17권2호
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• pp.499-505
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• 2006

In this note, we give an elementary simple new proof of the main result of $Full{\acute{e}}r$ and Majlender [Fuzzy Sets and systems 136 (2003) 203-215] concerning obtaining minimal variability OWA operator weights.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.635-639
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• 2004

In this paper, we present a novel approach for the structure of Fuzzy Neural Network(FNN) based on wavelet function and apply this network structure to the modeling of chaotic nonlinear systems. Generally, the wavelet fuzzy model(WFM) has the advantage of the wavelet transform by constituting the fuzzy basis function(FBF) and the conclusion part to equalize the linear combination of FBF with the linear combination of wavelet functions. However, it is very difficult to identify the fuzzy rules and to tune the membership functions of the fuzzy reasoning mechanism. Neural networks, on the other hand, utilize their learning capability for automatic identification and tuning. Therefore, we design a wavelet based FNN structure(WFNN) that merges these advantages of neural network, fuzzy model and wavelet transform. The basic idea of our wavelet based FNN is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. And our network can automatically identify the fuzzy rules by modifying the connection weights of the networks via the gradient descent scheme. To verify the efficiency of our network structure, we evaluate the modeling performance for chaotic nonlinear systems and compare it with those of the FNN and the WFM.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제4권1호
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• pp.111-118
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• 2004

In this paper, we present a novel approach for the structure of Fuzzy Neural Network(FNN) based on wavelet function and apply this network structure to the solution of the tracking problem for mobile robots. Generally, the wavelet fuzzy model(WFM) has the advantage of the wavelet transform by constituting the fuzzy basis function(FBF) and the conclusion part to equalize the linear combination of FBF with the linear combination of wavelet functions. However, it is very difficult to identify the fuzzy rules and to tune the membership functions of the fuzzy reasoning mechanism. Neural networks, on the other hand, utilize their learning capability for automatic identification and tuning. Therefore, we design a wavelet based FNN structure(WFNN) that merges these advantages of neural network, fuzzy model and wavelet transform. The basic idea of our wavelet based FNN is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. And our network can automatically identify the fuzzy rules by modifying the connection weights of the networks via the gradient descent scheme. To verify the efficiency of our network structure, we evaluate the tracking performance for mobile robot and compare it with those of the FNN and the WFM.

• Communications for Statistical Applications and Methods
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• 제19권3호
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• pp.359-365
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• 2012

The fuzzy set framework has a number of properties that make it suitable to formulize uncertain information in medical diagnosis. This study introduces a fuzzy diagnostic method based on the interval-valued interview chart and the interval-valued intuitionistic fuzzy weighted arithmetic average(IIFWAA) operator. An issue in the use of the IIFWAA operator is to determine the weights. In this study, we propose the occurrence information of symptoms as the weights. An illustrative example is provided to demonstrate its practicality and effectiveness.

• 한국지능시스템학회논문지
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• 제12권3호
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• pp.261-268
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• 2002

This paper represents that auto tunings of membership functions and weights in the fuzzy neural networks are effectively performed by immune algorithm. A number of hybrid methods in fuzzy-neural networks are considered in the context of tuning of learning method, a general view is provided that they are the special cases of either the membership functions or the gain modification in the neural networks by genetic algorithms. On the other hand, since the immune network system possesses a self organizing and distributed memory, it is thus adaptive to its external environment and allows a PDP (parallel distributed processing) network to complete patterns against the environmental situation. Also, it can provide optimal solution. Simulation results reveal that immune algorithms are effective approaches to search for optimal or near optimal fuzzy rules and weights.

• International Journal of Quality Innovation
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• 제7권2호
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• pp.83-96
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• 2006

In this paper, fuzzy hierarchical analysis (FHA) is used to explore the process by which the criteria weights of the Taiwan National Quality Award (TNQA) are assigned by TNQA committee members. Each member is allowed to employ fuzzy scales in place of exact scales. Each pairwise comparison of criteria is made through a questionnaire from each TNQA committee member. The membership function of trapezoidal fuzzy numbers is introduced to specify TNQA committee members' intentions. After FHA, the reasonable range of each criterion weight of TNQA is determined. The current criteria weights of TNQA are properly verified.

• 한국정보시스템학회:학술대회논문집
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• 한국정보시스템학회 1996년도 추계학술발표회 발표논문집
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• pp.211-216
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• 1996

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 o 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 솜 t 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

• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 1996년도 추계 학술 발표회 발표논문집
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• pp.211-216
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• 1996

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 nerual 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.