On comparing interval numbers

Sengupta, Atanu; Pal, Tapan Kumar

doi:10.1016/s0377-2217(99)00319-7

Cited by 432 publications

(186 citation statements)

References 11 publications

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“…The X control chart for the LEDs fabrication process. 21, the manufacturing process is categorized as rather out of control with degree  =0.36. In these cases, if the setup cost is small (or tolerable for the decision-maker), intervening the manufacturing process is suggested.…”

Section: A Practical Examplementioning

confidence: 99%

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Interval-valued process data monitoring and controlling

Hsu

Kung

Shu³

2013

AIR

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Statistical process control, a recognized technique for improving quality and productivity, has been widely employed throughout various industries. The conventional Shewhart control charts are applicable only when the collected sample data are real-valued data. For the purpose of controlling uncertain information when interval-valued data inevitably appear in the manufacturing or service processes, in this paper an interval-data analysis methodology is first applied. We construct Shewhart control charts whose control limits, consequently as interval numbers, are obtained by using the united extension principle, which is an effective method for dealing with closed interval data. Then, to identify the special causes of variation and alarm the requirement for corrective actions, we propose new rules for classifying current conditions of the manufacturing process based on an acceptability function of two interval numbers constructed from interval-valued sample data. Finally, the proposed methodologies are illustrated by practical examples to show their potential applications.

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Section: A Practical Examplementioning

confidence: 99%

“…Segupta and Pal [21] indicated interval numbers can be thought as the extension of real numbers as well as subsets of the real line  . They convey the extent of tolerance that the key quality characteristic can possibly take.…”

Section: Introductionmentioning

confidence: 99%

Interval-valued process data monitoring and controlling

Hsu

Kung

Shu³

2013

AIR

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“…Recent studies have also developed numerous statistical analysis methods and concepts for fuzzy theory (Fan, 2010;Hsu, Tsai & Chiang, 2009;Ravi, Shankar, Sireesha, Rao & Vani, 2010;Sun & Wu, 2007;Wang & Chen, 2010;Wu & Lin, 2002). The second part involves the examining of the interval fuzzy number method to expand the applicability of the theory (Chu & Lin, 2009;Hung, Vladik, Wu & Gang, 2011;Lin & Chen, 2004;Sengupta & Pal, The Study of Taiwan's New Immigrant Children's Mathematics Achievement 37 2000; Yager, Detyniecki & Bouchon-Meunier, 2001). The third part includes research documents on the sorting of fuzzy numbers.…”

Section: Studies On the Application Of Fuzzy Statistics And Interval mentioning

confidence: 99%

The Study of Taiwan's New Immigrant Children's Mathematics Achievement

Lai¹,

Cheng²,

Lu³

2013

Research in Mathematical Education

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“…However, all these require specific probability distribution in modeling both model parameters and model structure. In the literature, previous non-probabilistic methods of sensitivity analysis are developed [16,17]. Several studies have confirmed the robustness of use of Hartley-like measure to apply sensitivity analysis in fuzzy theory framework in numerous fields [33][34][35].…”

Section: Introductionmentioning

confidence: 98%

Towards an uncertainty reduction framework for land-cover change prediction using possibility theory

Ferchichi

Boulila

Farah

2016

Vietnam J Comput Sci

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This paper presents an approach for reducing uncertainty related to the process of land-cover change (LCC) prediction. LCC prediction models have, almost, two sources of uncertainty which are the uncertainty related to model parameters and the uncertainty related to model structure. These uncertainties have a big impact on decisions of the prediction model. To deal with these problems, the proposed approach is divided into three main steps: (1) an uncertainty propagation step based on possibility theory is used as a tool to evaluate the performance of the model; (2) a sensitivity analysis step based on Hartley-like measure is then used to find the most important sources of uncertainty; and (3) a knowledge base based on machine learning algorithm is built to identify the reduction factors of all uncertainty sources of parameters and to reshape their values to reduce in a significant way the uncertainty about future changes of land cover. In this study, the present and future growths of two case studies were anticipated using multi-temporal Spot-4 and Landsat satellite images. These data are used for the preparation of prediction map of year 2025. The results show that our approach based on possibility theory has a potential for reducing uncertainty in LCC prediction modeling. B Ahlem Ferchichi

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On comparing interval numbers

Cited by 432 publications

References 11 publications

Interval-valued process data monitoring and controlling

Interval-valued process data monitoring and controlling

The Study of Taiwan's New Immigrant Children's Mathematics Achievement

Towards an uncertainty reduction framework for land-cover change prediction using possibility theory

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