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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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Journal ArticleDOI
01 Sep 2010
TL;DR: The main novelties of the proposed procedure have been developed for Generalized Fuzzy Numbers (GFNs), and the proposed algorithm has efficiently been applied in assessment of traffic police centers which is treated as a FMCGDMP.
Abstract: In this paper we have presented a TOPSIS approach based on preference ratio and an efficient fuzzy distance measurement for a Fuzzy Multiple Criteria Group Decision-Making Problem (FMCGDMP). Preference ratio with a moderate modification for negative fuzzy numbers was used as an efficient ranking method for fuzzy numbers in a relative manner. As human reasoning persuades that distances between two fuzzy numbers should be a fuzzy measure, so all distances between fuzzy numbers (i.e. distances between alternatives, Fuzzy Positive Ideal solutions, and Fuzzy Negative Ideal solutions) have been calculated as fuzzy numbers using an efficient fuzzy distance measurement. The aforementioned arguments make the proposed algorithm unique and well posed for real-life problem modeling. Moreover, the main novelties of the proposed procedure (i.e. the fuzzy distance measurement and Preference Ratio) have been developed for Generalized Fuzzy Numbers (GFNs). The proposed algorithm has efficiently been applied in assessment of traffic police centers which is treated as a FMCGDMP.

144 citations

Journal ArticleDOI
TL;DR: In this article, an adaptive neuro-fuzzy inference system (ANFIS) was used for the modeling of ground-coupled heat pump (GCHP) system in a test room with 16.24m 2 floor area in Firat University, Elazig (38.41°N, 39.14°E), Turkey, was designed and constructed.
Abstract: The aim of this study is to demonstrate the usefulness of an adaptive neuro-fuzzy inference system (ANFIS) for the modelling of ground-coupled heat pump (GCHP) system. The GCHP system connected to a test room with 16.24 m 2 floor area in Firat University, Elazig (38.41°N, 39.14°E), Turkey, was designed and constructed. The heating and cooling loads of the test room were 2.5 and 3.1 kW at design conditions, respectively. The system was commissioned in November 2002 and the performance tests have been carried out since then. The average performance coefficients of the system (COPS) for horizontal ground heat exchanger (GHE) in the different trenches, at 1 and 2 m depths, were obtained to be 2.92 and 3.2, respectively. Experimental performances were performed to verify the results from the ANFIS approach. In order to achieve the optimal result, several computer simulations have been carried out with different membership functions and various number of membership functions. The most suitable membership function and number of membership functions are found as Gauss and 2, respectively. For this number level, after the training, it is found that root-mean squared (RMS) value is 0.0047, and absolute fraction of variance ( R 2 ) value is 0.9999 and coefficient of variation in percent (cov) value is 0.1363. This paper shows that the values predicted with the ANFIS, especially with the hybrid learning algorithm, can be used to predict the performance of the GCHP system quite accurately.

143 citations

Journal ArticleDOI
01 Mar 2012
TL;DR: A new algorithm is proposed for solving a special type of fuzzy transportation problems by assuming that a decision maker is uncertain about the precise values of transportation cost only but there is no uncertainty about the supply and demand of the product.
Abstract: In the literature, several algorithms are proposed for solving the transportation problems in fuzzy environment but in all the proposed algorithms the parameters are represented by normal fuzzy numbers. Chen [Operations on fuzzy numbers with function principal, Tamkang Journal of Management Science 6 (1985) 13-25] pointed out that in many cases it is not to possible to restrict the membership function to the normal form and proposed the concept of generalized fuzzy numbers. There are several papers in the literature in which generalized fuzzy numbers are used for solving real life problems but to the best of our knowledge, till now no one has used generalized fuzzy numbers for solving the transportation problems. In this paper, a new algorithm is proposed for solving a special type of fuzzy transportation problems by assuming that a decision maker is uncertain about the precise values of transportation cost only but there is no uncertainty about the supply and demand of the product. In the proposed algorithm transportation costs are represented by generalized trapezoidal fuzzy numbers. To illustrate the proposed algorithm a numerical example is solved and the obtained results are compared with the results of existing approaches. Since the proposed approach is a direct extension of classical approach so the proposed approach is very easy to understand and to apply on real life transportation problems for the decision makers.

143 citations

BookDOI
01 Jan 1999

143 citations

Journal ArticleDOI
TL;DR: Two models based on multivariate Gaussian random fields are proposed to model this fuzzy membership process of mixed-pixel data, which involves predicting the group membership and estimating the parameters.
Abstract: In the usual statistical approach to spatial classification, it is assumed that each pixel belongs to precisely one of a small number of known groups. This framework is extended to include mixed-pixel data; then, only a proportion of each pixel belongs to each group. Two models based on multivariate Gaussian random fields are proposed to model this fuzzy membership process. The problems of predicting the group membership and estimating the parameters are discussed. Some simulations are presented to study the properties of this approach, and an example is given using Landsat remote-sensing data. >

143 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202353
2022123
2021340
2020354
2019385
2018433