Trends in Fuzzy Statistics

Taheri, S. Mahmoud

doi:10.17713/ajs.v32i3.459

Cited by 94 publications

(12 citation statements)

References 136 publications

(97 reference statements)

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“…A survey about fuzzy regression can be found in [4]. An optimal fuzzy model, which is considered in this study, given the set of observations, , ,..., 1 , ,..., , 1 m j x x y jn j j find such as [2,5].…”

Section: Fuzzy Regression Modelmentioning

confidence: 99%

Comparison of Statistical Regression, Fuzzy Regression and Artificial Neural Network Modeling Methodologies in Polyester Dyeing

Nasiri

Shanbeh

Tavanai

International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on In

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The aim of this study is to investigate, apply and compare statistical regression, fuzzy regression and Artificial Neural Network (ANN) for modeling the color yield in polyester high temperature (HT) dyeing as a function of disperse dyes concentration, temperature and time. The predictive power of the obtained models was evaluated by means of MSE value. The results showed that the model based on statistical regression did not meet the required conditions to be accepted. However, the ANN model with a minimum MSE showed a better predictive capability than the model based on fuzzy regression, although the fuzzy regression model was also acceptable.

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Section: Fuzzy Regression Modelmentioning

confidence: 99%

Comparison of Statistical Regression, Fuzzy Regression and Artificial Neural Network Modeling Methodologies in Polyester Dyeing

Nasiri

Shanbeh

Tavanai

International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on In

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“…The peaks for the cites per year and cites per paper and its relative measures per author and article are explainable by the citation statistics of a few articles. For example, the peak in 2013 is related to the article Taheri (2013), which is cited 158 times. We rank all articles according to their number of citations.…”

Section: Illustrative Examplementioning

confidence: 99%

Modeling and Prediction of the Impact Factor of Journals Using Open-Access Databases

Templ

2020

AJS

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This article is motivated by the work as editor-in-chief of the Austrian Journal of Statistics and contains detailed analyses about the impact of the Austrian Journal of Statistics. The impact of a journal is typically expressed by journal metrics indicators. One of the important ones, the journal impact factor is calculated from the Web of Science (WoS) database by Clarivate Analytics. It is known that newly established journals or journals without membership in big publishers often face difficulties to be included, e.g., in the Science Citation Index (SCI) and thus they do not receive a WoS journal impact factor, as it is the case for example, for the Austrian Journal of Statistics. In this study, a novel approach is pursued modeling and predicting the WoS impact factor of journals using open access or partly open-access databases, like Google Scholar, ResearchGate, and Scopus. I hypothesize a functional linear dependency between citation counts in these databases and the journal impact factor. These functional relationships enable the development of a model that may allow estimating the impact factor for new, small, and independent journals not listed in SCI. However, only good results could be achieved with robust linear regression and well-chosen models. In addition, this study demonstrates that the WoS impact factor of SCI listed journals can be successfully estimated without using the Web of Science database and therefore the dependency of researchers and institutions to this popular database can be minimized. These results suggest that the statistical model developed here can be well applied to predict the WoS impact factor using alternative open-access databases.

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“…Sejak diperkenalkannya teori himpunan fuzzy oleh L. A. Zadeh pada 1965, telah banyak artikel/paper yang membahas penggunaan teori fuzzy dalam bidang statistik dan probability. Di antaranya adalah regresi fuzzy, hypotesis fuzzy, variabel acak fuzzy, dan sebagainya (Taheri, 2003). Demikian juga dalam bidang pengendalian kualitas statistik, Youngtin (1996) memperkenalkan batas spesifikasi sebagai suatu bilangan fuzzy, dan Jorg Hoppner & Hans Wolf (1995) mengkonstruksi suatu bagan kendali Shewhart-Fuzzy.…”

Section: Pendahuluanunclassified

Penggunaan Bilangan Fuzzy Segitiga pada Perbandingan Kemampuan Proses

Abdy¹

2018

jmsk

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Kebanyakan metode-metode untuk menilai kemampuan proses manufaktur menggunakan bilangan biasa. Dalam tulisan ini dibahas metode untuk menilai kemampuan proses (Perbandingan Kemampuan Proses) dengan menggunakan bilangan fuzzy yang mempunyai fungsi keanggotaan berbentuk segitiga. Perbandingan Kemampuan Proses (PKP) tersebut diperlukan apabila batas spesifikasi yang diberikan adalah fuzzy. Beberapa hubungan di antara PKP fuzzy tersebut juga akan diberikan

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Trends in Fuzzy Statistics

Abstract: After introducing and developing fuzzy set theory, a lot of studies have been done to combine statistical methods and fuzzy set theory. This works, called fuzzy statistics, have been developed in some branches.

Cited by 94 publications

References 136 publications

Comparison of Statistical Regression, Fuzzy Regression and Artificial Neural Network Modeling Methodologies in Polyester Dyeing

Comparison of Statistical Regression, Fuzzy Regression and Artificial Neural Network Modeling Methodologies in Polyester Dyeing

Modeling and Prediction of the Impact Factor of Journals Using Open-Access Databases

Penggunaan Bilangan Fuzzy Segitiga pada Perbandingan Kemampuan Proses

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