G(1,1,λ,ρ) Optimization Model and Application Based on the FS

2014 ◽  
Vol 644-650 ◽  
pp. 1494-1497
Author(s):  
Han Lin Wang ◽  
Zi Hui Ren ◽  
Li Xia Xue ◽  
Yan Li Luo
Keyword(s):  
Prediction Model ◽  
Optimization Model ◽  
Experimental Results ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Model Based

A grey prediction model based on Free Searching (FS) () is proposed in this paper. Firstly, FS is applied to optimize the parameters of the model. The convergence of the FS algorithm is proved in order to show the reasonable of optimization with FS. Then, we give the factors which affect the precision of the prediction by analyzing the model. Based on this, the initial array is transformed. Finally, we predict several times used model and obtain the average of the prediction results’ combination. The experimental results show that the model is feasible, reasonable and effective.

scholarly journals Application of a Fractional Grey Prediction Model Based on a Filtering Algorithm in Image Processing

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Mingyu Tong ◽  
Kailiang Shao ◽  
Xilin Luo ◽  
Huiming Duan
Keyword(s):  
Image Processing ◽  
Prediction Model ◽  
Grey System Theory ◽  
Grey Prediction ◽  
Grey System ◽  
Middle Point ◽  
Filtering Algorithm ◽  
Grey Correlation ◽  
Grey Prediction Model ◽  
Model Based

Image filtering can change or enhance an image by emphasizing or removing certain features of the image. An image is a system in which some information is known and some information is unknown. Grey system theory is an important method for dealing with this kind of system, and grey correlation analysis and grey prediction modeling are important components of this method. In this paper, a fractional grey prediction model based on a filtering algorithm by combining a grey correlation model and a fractional prediction model is proposed. In this model, first, noise points are identified by comparing the grey correlation and the threshold value of each pixel in the filter window, and then, through the resolution coefficient of the important factor in image processing, a variety of grey correlation methods are compared. Second, the image noise points are used as the original sequence by the filter pane. The grey level of the middle point is predicted by the values of the surrounding pixel points combined with the fractional prediction model, replacing the original noise value to effectively eliminate the noise. Finally, an empirical analysis shows that the PSNR and MSE of the new model are approximately 27 and 140, respectively; these values are better than those of the comparison models and achieve good processing effects.

scholarly journals The Optimized Multivariate Grey Prediction Model Based on Dynamic Background Value and Its Application

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Tongfei Lao ◽  
Xiaoting Chen ◽  
Jianian Zhu
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Small Sample ◽  
Grey Prediction ◽  
Dynamic Background ◽  
Background Values ◽  
Grey Prediction Model ◽  
Model Based

As a tool for analyzing time series, grey prediction models have been widely used in various fields of society due to their higher prediction accuracy and the advantages of small sample modeling. The basic GM (1, N) model is the most popular and important grey model, in which the first “1” stands for the “first order” and the second “N” represents the “multivariate.” The construction of the background values is not only an important step in grey modeling but also the key factor that affects the prediction accuracy of the grey prediction models. In order to further improve the prediction accuracy of the multivariate grey prediction models, this paper establishes a novel multivariate grey prediction model based on dynamic background values (abbreviated as DBGM (1, N) model) and uses the whale optimization algorithm to solve the optimal parameters of the model. The DBGM (1, N) model can adapt to different time series by changing parameters to achieve the purpose of improving prediction accuracy. It is a grey prediction model with extremely strong adaptability. Finally, four cases are used to verify the feasibility and effectiveness of the model. The results show that the proposed model significantly outperforms the other 2 multivariate grey prediction models.

Weapon equipment management cost prediction based on forgetting factor recursive GM (1,1) model

2019 ◽  
Vol 10 (1) ◽  
pp. 38-45
Author(s):  
Subing Liu ◽  
Yin Chunwu ◽  
Cao Dazhi
Keyword(s):  
Prediction Model ◽  
Recursive Estimation ◽  
Model Parameters ◽  
Grey Prediction ◽  
Forgetting Factor ◽  
Content Type ◽  
Grey Prediction Model ◽  
Model Based ◽  
New Information ◽  
Equipment System

Purpose The purpose of this paper is to provide a new recursive GM (1,1) model based on forgetting factor and apply it to the modern weapon and equipment system. Design/methodology/approach In order to distinguish the contribution of new and old data to the grey prediction model with new information, the authors add forgetting factor to the objective function. The purpose of the above is to realize the dynamic weighting of new and old modeling data, and to gradually forget the old information. Second, the recursive estimation algorithm of grey prediction model parameters is given, and the new information is added in real time to improve the prediction accuracy of the model. Findings It is shown that the recursive GM (1,1) model based on forgetting factor can achieve both high effectiveness and high efficiency. Originality/value The paper succeeds in proposing a recursive GM (1,1) model based on forgetting factor, which has high accuracy. The model is applied to the field of modern weapon and equipment system and the result the model is better than the GM(1,1) model. The experimental results show the effectiveness and the efficiency of the prosed method.

Non-equidistant non-homogenous grey prediction model with fractional accumulation and its application

2021 ◽  
pp. 1-14
Author(s):  
Jia-Nian Zhu ◽  
Xu-Chong Liu ◽  
Chong Liu
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Model Based ◽  
Whale Optimization ◽  
High Prediction ◽  
Best Parameters ◽  
Better Than

Non-equidistant non-homogenous grey model (abbreviated as NENGM (1,1, k) model) is a grey prediction model suitable for predicting time series with non-equal intervals. It is widely used in various fields of society due to its high prediction accuracy and strong adaptability. In order to further improve the prediction accuracy of the NENGM (1,1, k) model, the NENGM (1,1, k) model is optimized in terms of the cumulative order and background value of the NENGM (1,1, k) model, and a NENGM (1,1, k) model based on double optimization is established (abbreviated as FBNENGM (1,1, k) model), and the whale optimization algorithm is used to solve the best parameters of the model. In order to verify the feasibility and validity of the FBNENGM (1,1, k) model, the FBNENGM (1,1, k) model and other four prediction models are applied to three cases respectively, and three indexes commonly used to evaluate the performance of prediction models are used to distinguish. The results show that the prediction accuracy of the FBNENGM (1,1, k) model based on double optimization is better than other prediction models.

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