• Title/Summary/Keyword: 퍼지 측도치

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Fuzzy Measures Defined by the Semi-Normed Fuzzy Integrals (준 노름 퍼지 적분에 의해 정의된 퍼지 측도)

  • Kim, Mi-Hye;Lee, Soon-Seok
    • The Journal of the Korea Contents Association
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    • v.2 no.4
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    • pp.99-103
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    • 2002
  • In this paper, we investigate for how to define a fuzzy measure by using the semi-normed fuzzy integral of a given measurable function with respect to another given fuzzy measure when t-seminorm is continuous. Let (X, F, g) be a fuzzy measure space, h$\in$L$^\circ$(X), and $\top$ be a continuous t-seminorm.. Then the set function $\nu$ defined by $\nu$(A)=$\int _A$h$\top$g for any $A\in$F is a fuzzy measure on (X, F).

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A Study on the Competitiveness of ASEAN and Korea′s Container Ports In International Logistics Strategies (국제물류전략에 있어서 ASEAN과 한국의 컨테이너항만 경쟁력에 관한 연구)

  • Gim, Jin-Goo;Lee, Jong-In
    • Journal of Navigation and Port Research
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    • v.28 no.3
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    • pp.177-184
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    • 2004
  • The purpose of this study is to identify and evaluate the competitiveness of container ports in ASEAN(Association of Southeast Asian Nations) and Korea, which plays a leading role in basing the hub of international logistics strategies at the onset of the 21st century. Its ultimate purpose is to consider the relevant policy-making by comparing the competitiveness of ASEAN and Korea's container ports. This paper adopted the HFP method, which is an empirical analysis that evaluated the port competitiveness by quantifying it a, a qualitative attribute in the aforementioned area, where both ASEAN and Korea vie with each other for increasing container throughput. The results of this study showed that Singapore ranked the first in the subject of study in view of the competitiveness, followed by Busan(2) and Manila(2) as a leading group of the relevant ports in international logistics strategies. This analytic evaluation contributes to the empirical approach applied to policy-making by the HFP method, which is the newest research technique in social science through the comparative study of port competitiveness between ASEAN and Korea.

The Development of Water Pollution Evaluation System using Fussy Integral (퍼지 적분을 이용한 수질오염 평가 시스템 구현)

  • Song Young-Jun;Kim Mi-Hye;Chung Keun-Yook;Lee Sang-Seung;Park Sung-Hoon
    • Proceedings of the Korea Contents Association Conference
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    • 2005.05a
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    • pp.391-395
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    • 2005
  • In this paper, the new evaluation system for water quality pollution is implemented using fuzzy integral based on the conventional evaluation criterions and their evaluation factors and this is the first phase in the whole water quality pollution evaluation system development. In the final evaluation for water quality pollution the factors like BOD, COD, SS, T-N, and T-P are taken into overall accounts. It is found that the final evaluation can be represented in a linear combination of respective factor evaluation when each factor is independent one another, With respect to the combination patterns the fuzzy measurement is defined and the fuzzy integral is taken. As a result this approach shows stable and reliable evaluation for the water quality pollution evaluation system development.

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Evaluation of Interpretability for Generated Rules from ANFIS (ANFIS에서 생성된 규칙의 해석용이성 평가)

  • Song, Hee-Seok;Kim, Jae-Kyeong
    • Journal of Intelligence and Information Systems
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    • v.15 no.4
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    • pp.123-140
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    • 2009
  • Fuzzy neural network is an integrated model of artificial neural network and fuzzy system and it has been successfully applied in control and forecasting area. Recently ANFIS(Adaptive Network-based Fuzzy Inference System) has been noticed widely among various fuzzy neural network models because of outstanding performance of control and forecasting accuracy. ANFIS has capability to refine its fuzzy rules interactively with human expert. In particular, when we use initial rule structure for machine learning which is generated from human expert, it is highly probable to reach global optimum solution as well as shorten time to convergence. We propose metrics to evaluate interpretability of generated rules as a means of acquiring domain knowledge and compare level of interpretability of ANFIS fuzzy rules to those of C5.0 classification rules. The proposed metrics also can be used to evaluate capability of rule generation for the various machine learning methods.

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