Triangular fuzzy series forecasting based on grey model and neural network

Zeng, Xiangyan; Shu, Lan; Huang, Guimin; Jiang, Jing

doi:10.1016/j.apm.2015.08.009

Cited by 48 publications

(15 citation statements)

References 16 publications

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“…To verify the performance of the proposed model, this paper used grey neural network architecture [16][17][18][19]. Fig.…”

Section: Health Index and Proposed Neural Network Architecturementioning

confidence: 99%

On-line prediction remaining useful life for ball bearings via grey NARX

Niu¹,

Tong²,

Cao³

et al. 2019

J. vibroeng.

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The Huge vibration data are generated continuously by many sensors in daily high-speed rotating machinery operations. Accurate online prediction based on big vibration data streaming can reduce the risks related to failures and avoid service disruptions. This paper presents a hybrid nonlinear autoregressive network with exogenous inputs (NARX) model to forecast the remaining useful life of ball bearings through health index based on big vibration data streaming. This approach is validated by a real data from PRONOSTIA experimentation platform and industrial test rig compared with backpropagation neural network (BP), Elman and general regression neural network (GRNN) prediction model. Root mean square error, mean absolute error and correlation coefficient were used as performance indexes to evaluate the prediction accuracy of these models. The mean absolute error, the root mean square error and the correlation coefficient of hybrid NARX model evaluation index are 2.04, 2.85 and 0.98 respectively. It shows that the model presented in this paper has higher prediction accuracy. It can meet the needs of actual ball bearing remaining useful life prediction and also provide reference in other fields.

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“…To verify the performance of the proposed model, this paper used grey neural network architecture [16][17][18][19]. Fig.…”

Section: Health Index and Proposed Neural Network Architecturementioning

confidence: 99%

On-line prediction remaining useful life for ball bearings via grey NARX

Niu¹,

Tong²,

Cao³

et al. 2019

J. vibroeng.

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show abstract

“…e model and its extensions have succeeded applied to the fields of business [14], energy [15][16][17][18], environment [19][20][21], industry [22,23], engineering [24], medicine [25], hydraulics [26], economics [27][28][29][30], and many other domains.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy-Decomposition Grey Modeling Procedure for Management Decision Analysis

Guo

Chang

Huang

et al. 2021

Mathematical Problems in Engineering

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To cope with the increasingly fierce market competition environment, enterprises need to quickly respond to business issues and maintain business advantages, which require timely and correct decisions. In this context, the general mathematical modeling method may cause overfitting phenomenon when using small data sets, so it is difficult to ensure good analysis performance. Therefore, it is significant for enterprises to use limited samples to analyze and forecast. Over the past few decades, the grey model and its extensions have been shown to be effective tools for processing small data sets. To further enforce the effectiveness of data uncertainty processing, a fuzzy-decomposition modeling procedure for grey models is developed. Specifically, Latent Information (LI) function is employed to decompose the initial series into three subseries; next, the three subseries are used to build three grey models and create the estimated values of the three subseries; finally, the weighted average method is applying to combine the estimated values of the three subseries into a single final predicted value. After the actual test on the monthly demand data of the thin-film transistor liquid crystal display panels, the proposed fuzzy-decomposition modeling procedure can result in good prediction outcomes and is thus an appropriate decision analysis tool for managers.

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“…To improve the prediction accuracy of the GM(1,1) model, many researchers have carried out a lot of works from di erent aspects, such as nding new accumulation generating operators [14][15][16][17][18][19], constructing more accurate background value formula [20,21], choosing parameter optimization methods [22], improving initial guess [23], and reducing residuals based on Fourier analysis and Markov chain [9,20]. Recently, some nonhomogeneous, nonlinear, hybrid, and multivariable grey models are proposed, see [6,[24][25][26] for examples. Modeling mechanism analysis can be found in [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

A Novel Weighted Fractional GM(1,1) Model and Its Applications

et al. 2020

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Based on the ideas of the new information priority principle and the fractional-accumulation generating operator, in this paper we propose a novel weighted fractional GM(1,1) (WFGM(1,1)) prediction model. In the new model, the original sequence is first transformed by using the weighted fractional-accumulation generating operator, which involves two parameters. With special choices of these parameters, the proposed WFGM(1,1) model reduces to the classical GM(1,1) model and the fractional GM(1,1) (FGM(1,1)) model, as well as the new information priority GM(1,1) (NIPGM(1,1)) model studied recently. Stability property of the WFGM(1,1) model is studied in detail. In practice, the quantum particle swarm optimization algorithm is adopted to choose the quasi-optimal parameters for the new model so as to get the best fitting accuracy. Finally, four numerical examples from different practical applications are present. Numerical results show that the new proposed prediction model is very efficient and has both the best fitting accuracy and the best prediction accuracy compared with the GM(1,1) and the FGM(1,1) as well as the NIPGM(1,1) prediction models.

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Triangular fuzzy series forecasting based on grey model and neural network

Cited by 48 publications

References 16 publications

On-line prediction remaining useful life for ball bearings via grey NARX

On-line prediction remaining useful life for ball bearings via grey NARX

A Fuzzy-Decomposition Grey Modeling Procedure for Management Decision Analysis

A Novel Weighted Fractional GM(1,1) Model and Its Applications

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