Reliability prediction using an unequal interval grey model

Wang, Yuhong; Pohl, Edward A.; Dang, Yaoguo

doi:10.1109/rams.2010.5448063

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…It should be noted that GM(1,1)-P, also called the nonlinear grey Bernoulli model, is identical to GM(1,1) when r = 0. In particular, the power exponent makes GM(1,1)-P much more useful than GM(1,1) (Dang et al, 2016 ; Lu et al, 2016 ).…”

Section: Grey Predictionmentioning

confidence: 99%

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Demand forecasting of green metal materials using non-equidistant grey prediction with robust nonlinear interval regression analysis

2021

Environ Dev Sustain

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The main advantages of magnesium alloys are that they are lightweight, easy to recycle, and have high vibration absorption. These unique characteristics make magnesium alloys important green metal materials for manufacturing, especially for the automotive and 3C products industries. The developing trends of these related industries can be recognized by forecasting the demand for magnesium alloys. This study develops grey prediction power models to forecast the demand for such a promising green metal material. Grey prediction is an appropriate technique because available data regarding the demand for magnesium alloys are not in line with any statistical assumptions. In particular, because outliers might cause a deterioration of forecasting performance, a robust nonlinear interval regression analysis with neural networks is applied to detect outliers by estimating data intervals. Then, a power model is applied to the newly generated non-equidistant data sequence without outliers. Residual modification is further considered here to improve the forecasting performance of the power model. The forecasting abilities of the proposed grey residual modification models are verified using actual magnesium alloy demand data. The experimental results for ex-post testing show that the mean absolute percentage errors of the proposed models that can work on non-equidistant data were minimal among all considered models.

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Section: Grey Predictionmentioning

confidence: 99%

“…First, GM(1,1)-P and NGM(1,1)-P are set up by equidistant and non-equidistant sequences, respectively. Next, instead of 1-AGO, = ( x (1) ( t 1 ), x (1) ( t 2 ),…, x (1) ( t n )) = ( , ,…, ) for NGM(1,1)-P can be generated by the following AGO as (Dang et al, 2016 ; Liu et al, 2017 ) …”

Section: Grey Predictionmentioning

confidence: 99%