• Journal of KIISE:Software and Applications
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• v.27 no.7
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• pp.723-730
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• 2000

Fuzzy numbers represent fuzzy numeric values. However, it is difficult to clearly determine whether one fuzzy number is larger or smaller than other fuzzy numbers. Thus it is also difficult to determine the rank which a fuzzy number takes, or to select the k-th largest fuzzy number in a given set of fuzzy numbers. In this paper, we propose a fuzzy set based method to determine the rank of a fuzzy number and the k-th largest fuzzy number. The proposed method uses a given fuzzy greater-than relation which is defined on a set of fuzzy numbers. Our method describes the rank of a fuzzy number with a fuzzy set of ranks that the fuzzy number can take, and the k-th largest fuzzy number with a fuzzy set of fuzzy numbers which can be k-th ranked.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.30-32
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• 1998

퍼지숫자는 불명확한 값을 표현하기 때문에, 퍼지숫자의 비교결과 역시 불명확한 성질을 갖고 있다. 본 논문에서는 이러한 퍼지숫자의 비교결과에 존재하는 불명확성을 표현하기 위해서, 퍼지 만족도 함수를 제안한다. 퍼지 만족도 함수는 두 퍼지숫자를 비교하여 그 비교결과로 0과1사이의 퍼지집합을 출력한다. 즉, 어느 숫자가 다른 숫자보다 클(작을) 가능성을 단순히 0과1사이의 값이 아닌, 퍼지집합으로 표현한다. 퍼지 만족도 함수는 이전에 제안된 만족도 함수로부터 확장되었다. 본 논문에서는 만족도 함수를 간략히 소개하고, 이를 이용하여 퍼지 만족도 함수를 제안하며, 이를 퍼지숫자 비교에 적용한 예를 제시한다.

• Journal of KIISE:Software and Applications
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• v.28 no.12
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• pp.930-937
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• 2001

For ranking fuzzy numbers, comparisons between numbers are necessary However, the comparison results can be vague since fuzzy numbers represent vague numeric values. Thus, ranking results of fuzzy numbers which are based on comparisons between fuzzy numbers, could also be vague. This means that there could be several possible ranking sequences of fuzzy numbers. There have been proposed many ranking methods for fuzzy numbers. However, most of them generate only ranking sequence. In this paper, we present a ranking method for fuzzy numbers using the fuzzy satisfaction function, Our method generates several possible ranking sequences of the given fuzzy numbers using the fuzzy satisfaction function.

• Journal of KIISE:Software and Applications
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• v.26 no.3
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• pp.441-451
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• 1999

본 논문에서는 퍼지숫자를 정렬하는 새로운 방법을 제안한다. 제안하는 방법은 사용자의 관심도나 선호도를 반영할 수 있는 방법을 제공하며, 퍼지숫자의 전체적인 가능성분포를 고려하는 평가함수를 방법은 사용자가 제사한 퍼지 집합과 만족도 함수(satisfaction fuction)를 이용하여 정렬 대상이 되는 퍼지숫자를 평가한 후 그 평가값에 따라서 순위를 정하게 된다. 만족도 함수는 두 퍼지숫자의 비교를 위해서 이전에 제안된 방법으로 퍼지숫자의 전체적인 가능성을 고려하는 특징이 있다. 본 논문에서는 제안하는 방법을 퍼지숫자 정렬에 적용한 예와 기존의 방법과 비교한 결과를 보이며, 응용 예로서 의사결정의 문제에 적용한 결과를 제시한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.5
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• pp.14-20
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• 1998

This paper proposes a fuzzy comparison method called the fuzzy satisfaction function. It compares two fuzzy numbers and produces a fuzzy set on [O, 11 as the comparison result. It represents the possibility that a fuzzy number is greater(smal1er) than the other with a fuzzy set on [0, I]. It is extended from the satisfaction function which compares two fuzzy numbers and generates a value in [0, 11 as the result. This paper summarizes the satisfaction function and proposes the fuzzy satisfaction function. Some numerical examples are also presented in this paper.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.3-8
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• 1998

본 논문에서는 사용자의 관심도나 선호도를 반영하여, 퍼지숫자를 정렬하는 방법을 제안한다. 사용자는 자신의 관심도나 선호도를 퍼지집합으로 표현한다. 제안하는 방법은 사용자로부터 주어진 퍼지집합을 평가관점으로 이용하며, 평가함수로는 이전에 제안된 만족도 함수를 이용한다. 제안하는 방법이 관점에 따라 어떠한 결과를 주는지를 보기 위하여, 퍼지숫자 정렬에 적용한 예를 보인다.

• Journal of Internet Computing and Services
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• v.8 no.3
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• pp.99-107
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• 2007

In general, the reliabilities of the fuzzy system are represented and analyzed by real numbers between zero and one, fuzzy numbers, intervals of confidence, interval-valued fuzzy sets, vague sets, etc. This paper addresses the method to analyze the reliability of the fuzzy system for the weighted components with the weights reflected on the importance of weighted components in an system. The reliabilities and the weights of the weighted components in a fuzzy numbers and considers the weights of the weighted components in a fuzzy system, therefore, its execution is faster and more flexible than the conventional methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.3
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• pp.14-24
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• 1996

A fuzzy classifier which needs various analyses of features using genetic algorithms is proposed. The fuzzy classifier has a simple structure, which contains a classification part based on fuzzy logic theory and a rule generation ation padptu sing genetic algorithms. The rule generation part determines optimal fuzzy membership functions and inclusior~ or exclusion of each feature in fuzzy classification rules. We analyzed recognition rate of a specific object, then added finer features repetitively, if necessary, to the object which has large misclassification rate. And we introduce repetitive analyses method for the minimum size of string and population, and for the improvement of recognition rates. This classifier is applied to three examples of the classification of iris data, the discrimination of thyroid gland cancer cells and the recognition of confusing handwritten and printed numerals. In the recognition of confusing handwritten and printed numerals, each sample numeral is classified into one of the groups which are divided according to the sample structure. The fuzzy classifier proposed in this paper has recognition rates of 98. 67% for iris data, 98.25% for thyroid gland cancer cells and 96.3% for confusing handwritten and printed numeral!;.

• Journal of the Korea Society of Computer and Information
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• v.16 no.10
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• pp.63-71
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• 2011

We present a method to modify the recognition of neural networks by the fuzzy inference in a handwritten digit recognition with large deformations, and we verified the method by the experiment. The neural networks take long time in learning and recognize 100% on the learning pattern. But the neural networks don't show a good recognition on the testing pattern. So, we apply the modified method as the fuzzy inference. As a result, the recognition and false recognition of neural networks was improved 90.2% and 9.8% respectively at 89.6% and 10.4% initially. This approach decreased especially the false recognition on digit 3, 5. We used the density of digit to extract the fuzzy membership function in this experiment. But, because the handwritten digit have varified input patterns, we will get a better recognition by extracting varifed characteristics and applying the composite fuzzy inference. We also propose the application of fuzzy inference on matching the input pattern, than applying strictly the fuzzy inference.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.04a
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• pp.496-500
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• 1995

전문가 내지 구성원의 주관적인 의견에 의존하는 방법의 하나인 델파이법(Delphi Method)은 관련자료가 불충분한 중.장기 예측, 전략결정 등에 이용되고 있다. 이 방법을 더욱 발전시킨 퍼지 델파이법(Fuzzy Delphi Method)은 델파이법에 퍼지숫자(fuzzy number)의 개념을 도입하여 정확한 예측을 하고자 하는 것이다. 또한 이러한 예측치가 삼각 퍼지 숫자(Triangular Fuzzy Number)로 주어져 불확실성에 대한 예측과 의견종합을 쉽게 하며, 전문가에 의해 추정된 삼각 퍼지 숫자의 입력을 토애 그 추정치들의 비유사도(Dissemblance Index)와 퍼지거리(fuzzy distance)를 계산하고 간단한 그래프를 다시 전문가에게 피드백(feedback)할 수 있도록 나타내어지는 과정을 code화하여 전문가들로 하여금 다양한 정보를 통하여 좀 더 정확한 추정치를 예측하고자 한다.