Elicitation of membership functions: how far can theory take us?

Bi̇lgi̇ç, Taner; Türkşen, I.B.

doi:10.1109/fuzzy.1997.619736

Cited by 15 publications

(3 citation statements)

References 18 publications

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“…In the case study, although all parties experienced the impact simulations, different fuzzy-set-elicitation methods were employed which has implications for the interpretation of the membership functions derived (Bilgic and Turksen, 1997). The direct-estimation method corresponds to a`similarity' view of fuzzy sets and concerns the nature of fuzziness in the values of perceptions of one individual and the degree to which an impact matches his or her notions of the concept under consideration.…”

Section: Discussionmentioning

confidence: 99%

Fuzzy Sets and Simulated Environmental Change: Evaluating and Communicating Impact Significance in Environmental Impact Assessment

2007

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IntroductionSince its inception, the rationale underpinning environmental impact assessment (EIA) has been to provide an analysis of the potential significant environmental effects associated with major development proposals, and to communicate this information to decisionmakers and the wider public. The significance of environmental effects has long been identified as the most critical element of EIA (Duinker and Beanlands, 1986;Sadler, 1996), with impact assessment legislation, guidelines, and Environmental Impact Statements (EISs) making liberalöif somewhat nebulousöuse of the term. There are now over 100 EIA systems in existence worldwide (Wood, 2003) and EIA regulations have been in operation for over fifteen years in the United Kingdom, where around 600 EISs are now produced annually. (1) EIA may therefore be considered to be approaching maturity, and yet in spite of a substantial body of experience, our knowledge of the use and meaning of the central concept of`significance' remains surprisingly rudimentary.Since the foundation of modern EIA in the United States under the National Environmental Policy Act of 1969, the classic model of normative decisionmaking underpinning the EIA process has centred on the assumption that environmental information will be employed in an instrumentally rational manner. In practice, although appearing to adopt a technical`scientific' approach to appraisal, the evaluation of the significance of environmental effects in EIA is ultimately dependent upon the specific environmental and social context, and is heavily reliant on professional and intuitive judgment and subjective decisions (

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Section: Discussionmentioning

confidence: 99%

Fuzzy Sets and Simulated Environmental Change: Evaluating and Communicating Impact Significance in Environmental Impact Assessment

2007

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“…Consideration of membership values as similarity indicators is typically utilized in prototype theory, where membership is defined as the state of being similar to a category's representative [ 36 ]. Therefore, a membership function value may be utilized to measure an element's degree of resemblance to a given set [ 22 ].…”

Section: Membership Functionmentioning

confidence: 99%

A Review on the Rule-Based Filtering Structure with Applications on Computational Biomedical Images

Yin

Hadjiloucas

Sun

et al. 2022

Journal of Healthcare Engineering

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In this paper, we present rule-based fuzzy inference systems that consist of a series of mathematical representations based on fuzzy concepts in the filtering structure. It is crucial for understanding and discussing different principles associated with fuzzy filter design procedures. A number of typical fuzzy multichannel filtering approaches are provided in order to clarify the different fuzzy filter designs and compare different algorithms. In particular, in most practical applications (i.e., biomedical image analysis), the emphasis is placed primarily on fuzzy filtering algorithms, with the main advantages of restoration of corrupted medical images and the interpretation capability, along with the capability of edge preservation and relevant image information for accurate diagnosis of diseases.

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“…Our analysis considers the membership function as a function of similarity. Viewing membership values as similarity indicators is often used in prototype theory where membership is a notion of being similar to a representative of a category [52]. Thus a membership function value can be used to quantify the degree of similarity of an element to the set in question.…”

Section: Determining the Parametersmentioning

confidence: 99%