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Membership function
About: Membership function is a research topic. Over the lifetime, 15795 publications have been published within this topic receiving 418366 citations.
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TL;DR: In this article, a three-stage approach is proposed to measure technical efficiency in a fuzzy environment using the traditional data envelopment analysis framework and then merges concepts developed in fuzzy parametric programming (Carlsson and Korhonen, 1986).
Abstract: A three stage approach is proposed to measure technical efficiency in a fuzzy environment. This approach uses the traditional data envelopment analysis framework and then merges concepts developed in fuzzy parametric programming (Carlsson and Korhonen, 1986). In the first stage, vague input and output variables are expressed in terms of their risk-free and impossible bounds and a membership function. This membership function represents the degree to which a production scenario is plausible. In the second stage, conventional efficiency measurement models (Banker, Charnes and Cooper, 1984; Deprins, Simar and Tulkens, 1984) are re-formulated in terms of the risk-free and impossible bounds and the membership function for each of the fuzzy input and output variables. In the third stage, technical efficiency scores are computed for different values of the membership function so as to identify uniquely sensitive decision making units. The approach is illustrated in the context of a preprint and packaging manufacturing line which inserts commercial pamphlets into newspapers.
146 citations
TL;DR: This paper proposes a fractional programming approach to construct the membership function for fuzzy weighted average based on the α-cut representation of fuzzy sets and the extension principle, and a pair of fractional programs is formulated to find theα-cut of fuzzy weightedAverage.
146 citations
TL;DR: A way of formulating neuro-fuzzy approaches for both feature selection and extraction under unsupervised learning of a fuzzy feature evaluation index for a set of features is demonstrated.
Abstract: Demonstrates a way of formulating neuro-fuzzy approaches for both feature selection and extraction under unsupervised learning. A fuzzy feature evaluation index for a set of features is defined in terms of degree of similarity between two patterns in both the original and transformed feature spaces. A concept of flexible membership function incorporating weighted distance is introduced for computing membership values in the transformed space. Two new layered networks are designed. The tasks of membership computation and minimization of the evaluation index, through unsupervised learning process, are embedded into them without requiring the information on the number of clusters in the feature space. The network for feature selection results in an optimal order of individual importance of the features. The other one extracts a set of optimum transformed features, by projecting n-dimensional original space directly to n'-dimensional (n'
146 citations
TL;DR: In this paper, an adaptive network based fuzzy inference system (ANFIS) was used to predict the workpiece surface roughness after the end milling process, including spindle speed, feed rate and depth of cut.
146 citations
TL;DR: It is shown that the proposed control is continuous, guarantees global stability and the H ∞ performance index, and extensive simulations on the tracking control of a~two-link rigid robotics manipulator verify the effectiveness of the proposed algorithms.
146 citations