The definition system and computational rule of grey determinant and its application to n grey equations with n grey linear equations

Li, Qiaoxing

doi:10.1108/gs-02-2014-001

Cited by 18 publications

(36 citation statements)

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“…The development coefficient a reflects the developmental tide of the GM(1,1) and b represents the total external excitation 18 . These parameters can reflect the trend of the original data to some degree, and thus the grey model can be used to compress data, which is shown in Fig.…”

Section: The Gm-gp Algorithmmentioning

confidence: 99%

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Improved GP algorithm for the analysis of sleep stages based on grey model

Li¹,

Xie²,

Zhao³

et al. 2017

ScienceAsia

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Correlation dimension analysis of EEG signals is widely used to access sleep stages. However, the standard Grassberger-Procaccia (GP) algorithm used to calculate the correlation dimension is very time consuming. To overcome this problem, an algorithm that combines the grey model and GP algorithm (GM-GP) is proposed. The results show that the correlation dimensions computed from GP and GM-GP are highly correlated, and the significance between the CDs in different stages of GM-GP is similar to GP. Furthermore, the computation time of the proposed method is at most 5% of that of the GP. The proposed algorithm is suitable for the real-time monitoring of sleep stages, which can provide a deeper understanding of brain function.

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Section: The Gm-gp Algorithmmentioning

confidence: 99%

“…The grey model (GM), which was proposed by Deng, is primarily used for solving complicated mathematical problems and determining the implicit message of an uncertain or incomplete system 18 . The GM has evolved in many fields, such as forecasting, image denoising, which is achieved by reducing the amounts of data.…”

Section: Introductionmentioning

confidence: 99%

Improved GP algorithm for the analysis of sleep stages based on grey model

Li¹,

Xie²,

Zhao³

et al. 2017

ScienceAsia

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“…In recent years, the development tendency of traffic death toll in China has shown a saturated S-shaped process, so it was suitable to use Verhulst model to make prediction [ 12 ]. The fundamental and process of Verhulst model building are as follows.…”

Section: The Verhulst Model Of Traffic Death Tollmentioning

confidence: 99%

Combined Prediction Model of Death Toll for Road Traffic Accidents Based on Independent and Dependent Variables

Feng

Shi-sheng

Zhang

et al. 2014

Computational Intelligence and Neuroscience

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In order to build a combined model which can meet the variation rule of death toll data for road traffic accidents and can reflect the influence of multiple factors on traffic accidents and improve prediction accuracy for accidents, the Verhulst model was built based on the number of death tolls for road traffic accidents in China from 2002 to 2011; and car ownership, population, GDP, highway freight volume, highway passenger transportation volume, and highway mileage were chosen as the factors to build the death toll multivariate linear regression model. Then the two models were combined to be a combined prediction model which has weight coefficient. Shapley value method was applied to calculate the weight coefficient by assessing contributions. Finally, the combined model was used to recalculate the number of death tolls from 2002 to 2011, and the combined model was compared with the Verhulst and multivariate linear regression models. The results showed that the new model could not only characterize the death toll data characteristics but also quantify the degree of influence to the death toll by each influencing factor and had high accuracy as well as strong practicability.

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“…If the forecasting model is directly constructed based on original annual power load data, it may lead to a larger forecasting error, or the simulating effect is good but the forecasting effect is poor. For this reason, Liu [ 21 ] proposed the concept of buffer operator and established the GM (1,1) model after using the buffer operator to pre-process the historical annual power load data. So the smoothness of the modeling sequence is effectively improved, the randomness of the historical annual power load data is weakened and the overall trend of load development is strengthened, and the accuracy of GM (1,1) model is improved.…”

Section: Introductionmentioning

confidence: 99%

A VVWBO-BVO-based GM (1,1) and its parameter optimization by GRA-IGSA integration algorithm for annual power load forecasting

Wang

2018

PLoS ONE

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Annual power load forecasting is not only the premise of formulating reasonable macro power planning, but also an important guarantee for the safety and economic operation of power system. In view of the characteristics of annual power load forecasting, the grey model of GM (1,1) are widely applied. Introducing buffer operator into GM (1,1) to pre-process the historical annual power load data is an approach to improve the forecasting accuracy. To solve the problem of nonadjustable action intensity of traditional weakening buffer operator, variable-weight weakening buffer operator (VWWBO) and background value optimization (BVO) are used to dynamically pre-process the historical annual power load data and a VWWBO-BVO-based GM (1,1) is proposed. To find the optimal value of variable-weight buffer coefficient and background value weight generating coefficient of the proposed model, grey relational analysis (GRA) and improved gravitational search algorithm (IGSA) are integrated and a GRA-IGSA integration algorithm is constructed aiming to maximize the grey relativity between simulating value sequence and actual value sequence. By the adjustable action intensity of buffer operator, the proposed model optimized by GRA-IGSA integration algorithm can obtain a better forecasting accuracy which is demonstrated by the case studies and can provide an optimized solution for annual power load forecasting.

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The definition system and computational rule of grey determinant and its application to n grey equations with n grey linear equations

Cited by 18 publications

References 0 publications

Improved GP algorithm for the analysis of sleep stages based on grey model

Improved GP algorithm for the analysis of sleep stages based on grey model

Combined Prediction Model of Death Toll for Road Traffic Accidents Based on Independent and Dependent Variables

A VVWBO-BVO-based GM (1,1) and its parameter optimization by GRA-IGSA integration algorithm for annual power load forecasting

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