• Title/Summary/Keyword: fuzzy logic reasoning

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Truth function mapping (진리함수사상)

  • Park, Jin-Won;Kang, Sang-Jin;Yun, Yong-Sik
    • Journal of the Korean Institute of Intelligent Systems
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    • v.16 no.2
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    • pp.198-202
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    • 2006
  • In this paper, we introduce Baldwin's approximate reasoning with fuzzy logic and some truth function mappings usually used in Baldwin's method. And we introduce some assessment criteria for approximate reasonings and we define some truth function mappings which satisfy more criteria than those which are already known.

Preference-based Supply Chain Partner Selection Using Fuzzy Ontology (퍼지 온톨로지를 이용한 선호도 기반 공급사슬 파트너 선정)

  • Lee, Hae-Kyung;Ko, Chang-Seong;Kim, Tai-Oun
    • Journal of Intelligence and Information Systems
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    • v.17 no.1
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    • pp.37-52
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    • 2011
  • Supply chain management is a strategic thinking which enhances the value of supply chain and adapts more promptly for the changing environment. For the seamless partnership and value creation in supply chains, information and knowledge sharing and proper partner selection criteria must be applied. Thus, the partner selection criteria are critical to maintain product quality and reliability. Each part of a product is supplied by an appropriate supply partner. The criteria for selecting partners are technological capability, quality, price, consistency, etc. In reality, the criteria for partner selection may change according to the characteristics of the components. When the part is a core component, quality factor is the top priority compared to the price. For a standardized component, lower price has a higher priority. Sometimes, unexpected case occurs such as emergency order in which the preference may shift on the top. Thus, SCM partner selection criteria must be determined dynamically according to the characteristics of part and its context. The purpose of this research is to develop an OWL model for the supply chain partnership depending on its context and characteristics of the parts. The uncertainty of variable is tackled through fuzzy logic. The parts with preference of numerical value and context are represented using OWL. Part preference is converted into fuzzy membership function using fuzzy logic. For the ontology reasoning, SWRL (Semantic Web Rule Language) is applied. For the implementation of proposed model, starter motor of an automobile is adopted. After the fuzzy ontology is constructed, the process of selecting preference-based supply partner for each part is presented.

A Study on Focus Position Control of Reflector Using Fuzzy Controller (퍼지제어기를 이용한 반사경의 초점 위치제어에 관한 연구)

  • Jeong, Hoi-Seong;Kim, Jun-Su;Kim, Hye-Ran;Kim, Gwan-Hyung;Lee, Hyung-Ki
    • Journal of the Korean Institute of Intelligent Systems
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    • v.21 no.5
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    • pp.645-652
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    • 2011
  • The present study investigated the tracking system of a reflector to trace the movement of sun. The system was designed to minimize the error between the vertical vector of reflector and the position of sun. The proposed system was able to collect the sun lights at a point as a useful source of light energy and transmit the collected light to a remote area through optical fibers. Also the study successfully solved the controller design problem due to the complexity of modeling of the sun tracking system using a fuzzy logic controller which mimics human reasoning.

A Comparative Study of Estimation by Analogy using Data Mining Techniques

  • Nagpal, Geeta;Uddin, Moin;Kaur, Arvinder
    • Journal of Information Processing Systems
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    • v.8 no.4
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    • pp.621-652
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    • 2012
  • Software Estimations provide an inclusive set of directives for software project developers, project managers, and the management in order to produce more realistic estimates based on deficient, uncertain, and noisy data. A range of estimation models are being explored in the industry, as well as in academia, for research purposes but choosing the best model is quite intricate. Estimation by Analogy (EbA) is a form of case based reasoning, which uses fuzzy logic, grey system theory or machine-learning techniques, etc. for optimization. This research compares the estimation accuracy of some conventional data mining models with a hybrid model. Different data mining models are under consideration, including linear regression models like the ordinary least square and ridge regression, and nonlinear models like neural networks, support vector machines, and multivariate adaptive regression splines, etc. A precise and comprehensible predictive model based on the integration of GRA and regression has been introduced and compared. Empirical results have shown that regression when used with GRA gives outstanding results; indicating that the methodology has great potential and can be used as a candidate approach for software effort estimation.