• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.3
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• pp.224-229
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• 2010

In this paper, using fuzzy complex valued functions and fuzzy complex valued fuzzy measures ([11]) and interval-valued Choquet integrals ([2-6]), we define Choquet integral with respect to a fuzzy complex valued fuzzy measure of a fuzzy complex valued function and investigate some basic properties of them.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.563-566
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• 2003

Quality measures play an important role in the field of image processing. Such measures are commonly used to assess the performance of different algorithms that are designed to perform a specific image processing task. In this paper we propose two novel measures for image quality assessment based on the notion of correlation between fuzzy sets. Two different definitions fur the correlation between fuzzy sets have been used. In order to calculate the proposed quality measures two approaches were evaluated, one with direct application of the measures to the image′s pixels and the other using the fuzzy set corresponding to the normalized histogram of the image. A comparative study of the proposed measures is performed by investigating their behavior using images with different types of distortions, such as impulsive "salt at pepper" noise, additive white Gaussian noise, multiplicative speckle noise, blurring, gamma distortion, and JPEG compression.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.39-45
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• 2005

In this paper, we consider fuzzy measures and Choquet integrals. Also we discuss some results proved by us and new works.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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• pp.270-274
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• 2001

Conventional similarity measures suggested so far can be classified into three categories: (i) geometric similarity measures, (ij) set-theoretic similarity measures, and (iii) matching function-based similarity measures. On the basis of the characteristics of the conventional similarity measures, in this paper, we propose a new similarity measure of fuzzy sets and investigate its properLies. Finally, numelical examples are provided for the comparison of characteristics of the proposed similarity measure and other previous similarity measures.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.119-121
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• 1998

For a fuzzy system modeled by a fuzzy hypergraph, two fuzzy similarity measures are proposed:one for the fuzzy similarity between fuzzy sets and the other between elements in fuzzy sets. The proposed measures can represent the realistic similarities which can not be given by the existing measures. With an example, it is shown that it can be used in the system analysis.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.4
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• pp.275-280
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• 2010

Verification of efficiency in data management fuzzy entropy and similarity measure were discussed and verified by applying reliable data selection problem and numerical data similarity evaluation. In order to calculate the certainty or uncertainty fuzzy entropy and similarity measure are designed and proved. Designed fuzzy entropy and similarity are considered as dissimilarity measure and similarity measure, and the relation between two measures are explained through graphical illustration. Obtained measures are useful to the application of decision theory and mutual information analysis problem. Extension of data quantification results based on the proposed measures are applicable to the decision making and fuzzy game theory.

• Journal of the Korea Convergence Society
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• v.4 no.2
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• pp.35-41
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• 2013

Data management with fuzzy entropy and similarity measure were discussed and verified by applying reliable data selection problem. Calculation of certainty or uncertainty for data, fuzzy entropy and similarity measure are designed and proved. Proposed fuzzy entropy and similarity are considered as dissimilarity measure and similarity measure, and the relation between two measures are explained through graphical illustration.Obtained measures are useful to the application of decision theory and mutual information analysis problem. Extension of data quantification results based on the proposed measures are applicable to the decision making and fuzzy game theory.

• Journal of applied mathematics & informatics
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• v.27 no.3_4
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• pp.843-856
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• 2009

In possibility framework, we propose two risk measures named Fuzzy Value-at-Risk and Fuzzy Conditional Value-at-Risk, based on Credibility measure. Two portfolio optimization models for fuzzy portfolio selection problems are formulated. Then a chaos genetic algorithm based on fuzzy simulation is designed, and finally computational results show that the two risk measures can play a role in possibility space similar to Value-at-Risk and Conditional Value-at-Risk in probability space.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.307-313
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• 1995

Using the representation theorem of fuzzy number, we give the Radon-Nikodym theorem for fuzzy-number-valued measures

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1053-1056
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• 2004

In this article we have applied Schweizer & Sklars t-norm based similarity measures to classification task. We will compare results to fuzzy similarity measure based classification and show that sometimes better results can be found by using these measures than fuzzy similarity measure. We will also show that classification results are not so sensitive to p values with Schweizer & Sklars measures than when fuzzy similarity is used. This is quite important when one does not have luxury of tuning these kind of parameters but needs good classification results fast.