• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.5
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• pp.521-526
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• 2005

The fuzzy entropy is proposed for measuring of uncertainty with the help of relation between distance measure and similarity measure. The proposed fuzzy entropy is constructed through a distance measure. In this study, Hamming distance measure is employed for a distance measure. Also a similarity measure is constructed through a distance measure for the measure of similarity between fuzzy sets or crisp sets and the proposed fuzzy entropies and similarity measures are proved.

• Korean Management Science Review
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• v.14 no.1
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• pp.11-29
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• 1997

This paper presents the better efficient evaluation of the Small and Medium Enterprises by use of fuzzy multi-criteria decision making methodology under intersectional dependence relations. The five Small and Medium Enterprises alternative will be evaluated by Fuzzy Analytic Hierarchy Process(FAHP) based on entropy weight in this study. A case study is presented to illustrate the use of entropy weight measurement with intersectional dependence problems. These problems are evaluated seven criteria : market criteria, thchnology criteria, management ability criteria, planning criteria, propulsion ability criteria, project propulsion basis criteria, propulsion result criteria.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.12
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• pp.985-992
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• 2001

This paper proposes a self-organizing fuzzy modeling(SOFUM)which an create a new hyperplane shaped cluster and adjust parameters of the fuzzy model in repetition. The suggested algorithm SOFUM is composed of four steps: coarse tuning. fine tuning cluster creation and optimization of learning rates. In the coarse tuning fuzzy C-regression model(FCRM) clustering and weighted recursive least squared (WRLS) algorithm are used and in the fine tuning gradient descent algorithm is used to adjust parameters of the fuzzy model precisely. In the cluster creation, a new hyperplane shaped cluster is created by applying multiple regression to input/output data with relatively large fuzzy entropy based on parameter tunings of fuzzy model. And learning rates are optimized by utilizing meiosis-genetic algorithm in the optimization of learning rates To check the effectiveness of the suggested algorithm two examples are examined and the performance of the identified fuzzy model is demonstrated via computer simulation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.335-338
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• 2004

본 논문에서는 기존의 정보량(Entropy) 기반 클러스터링 기법을 향상시키기 위한 방법으로서 퍼지 정보량을 이용하였다 가우시안 혼합 모델을 이용하면, 프로토타입의 목적 함수를 이용하는 클러스터링 기법보다 향상된 결과를 얻을 수 있고, Parameter의 조정이 요구되지 않는다. 그러나, 가우시안 혼합 모델의 사용은 주어진 패턴 집합을 클러스터링하는데 계산량의 증가를 초래하게 된다. 본 논문에서는 가우시안 혼합 모델의 정형화에 요구되는 계산량을 감소시키는 방법을 제시한다 또한 퍼지정보량(Fuzzy Entropy)을 적용하여 기존의 정보량 기반의 클러스터링 결과와 비교 분석하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.1
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• pp.76-81
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• 2016

In this paper, we present the entropy and similarity measure for generalized hesitant fuzzy information, and discuss their desirable properties. Some measure formulas are developed, and the relationships among them are investigated. We show that the similarity measure and entropy for generalized hesitant fuzzy information can be transformed by each other based on their axiomatic definitions. Furthermore, an approach of multiple attribute decision making problems where attribute weights are unknown and the evaluation values of attributes for each alternative are given in the form of GHFEs is investigated.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.200-205
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• 2008

For data analysis, fuzzy entropy is introduced as the measure of fuzziness, similarity measure is also constructed to represent similarity between data. Similarity measure between fuzzy membership functions is constructed through distance measure, and the proposed similarity measure are proved. Application of proposed similarity measure to the example of reliable data selection is also carried out. Application results are compared with the previous results that is obtained through fuzzy entropy and statistical knowledge.

• Journal of the Korea Safety Management & Science
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• v.16 no.4
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• pp.229-236
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• 2014

Failure modes and effects analysis (FMEA) is a widely used engineering tool in the fields of the design of a product or a process to improve its quality or performance by prioritizing potential failure modes in terms of three risk factors-severity, occurrence, and detection. In a classical FMEA, the risk priority number is obtained by multiplying the three values in 10 score scales which are evaluated for the three risk factors. However, the drawbacks of the classical FMEA have been mentioned by many previous researchers. As a way to overcome these difficulties, this paper suggests the ELECTRE III that is a representative technique among outranking models. Furthermore, fuzzy linguistic variables are included to deal with ambiguous and imperfect evaluation process. In addition, when the importances for the three risk factors are obtained, the entropy method is applied. The numerical example which was previously studied by Kutlu and Ekmekio$\breve{g}$lu(2012), who suggested the fuzzy TOPSIS method along with fuzzy AHP, is also adopted so as to be compared with the results of their research. Finally, after comparing the results of this study with that of Kutlu and Ekmekio$\breve{g}$lu(2012), further possible researches are mentioned.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.4 s.310
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• pp.1-8
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• 2006

This paper proposes an improved FCM(Fuzzy C-means) algorithm using intercluster and entropy-based weight in gray image. The fuzzy clustering methods have been extensively used in the image segmentation since it extracts feature information of the region. Most of fuzzy clustering methods have used the FCM algorithm. But, FCM algorithm is still sensitive to noise, as it does not include spatial information. In addition, it can't correctly classify pixels according to the feature-based distributions of clusters. To solve these problems, we applied a weight and intercluster to the traditional FCM algorithm. A weight is obtained from the entropy information based on the cluster's number of neighboring pixels. And a membership for one pixel is given based on the information considering the feature-based intercluster. Experiments has confirmed that the proposed method was more tolerant to noise and superior to existing methods.

• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.225-227
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• 2006

The transmission network clustering using a fuzzy entropy function are proposed in this paper. We can define a similarity measure through a fuzzy entropy. All node in the transmission network system has its own values indicating the physical characteristics of that system and the similarity measure in this paper is defined through the system-wide characteristic values at each node. However, to tackle the geometric mis-clustering problem, that is, to avoid the clustering of geometrically distant locations with similar measures, the locational informations are properly considered and incorporated in the proposed similarity measure. In this paper, a new regional clustering measure for the transmission network system is proposed and proved. The proposed measure is verified through IEEE 39 bus system.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.1
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• pp.52-57
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• 1999

In this paper, the methods which calculate the tuple membership values in a vague model of the fuzzy databases are analyzed A method among them is proposed to offer the effective solutions to the users. Also an information theory is studied to calculate the entropy of the results of a fuzzy query and an algorithm is proposed to control the size of the entropy.