scholarly journals Prediction and Optimization of Electrospun Polyacrylonitrile Fiber Diameter Based on Grey System Theory

Materials ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2237 ◽  
Author(s):  
Qihong Zhou ◽  
Liqun Lin ◽  
Ge Chen ◽  
Zhaoqun Du
Keyword(s):  
System Theory ◽  
Prediction Error ◽  
Fiber Diameter ◽  
Grey System Theory ◽  
Grey Prediction ◽  
Grey System ◽  
Average Prediction Error ◽  
Background Value ◽  
High Prediction ◽  
Fitting Error

This paper provides a new method for predicting the diameter of electrospun nanofibers. Based on the grey system theory, the effects of polyacrylonitrile mass fraction, voltage, flow rate, and receiving distance on fiber diameter were studied. The GM(1,1) (grey model) model and DNGM(1,1) (The DNGM (1,1) model is based on the whitening differential equation using parameters to Directly estimate the approximate Non-homogeneous sequence Grey prediction Model) model were established to predict fiber diameter by a single-factor change, and the results showed high prediction accuracy. The multi-variable grey model MGM(1,n) (MGM(1,n) is a Multivariate Grey prediction Model) was used for prediction of fiber diameter when multiple factors change simultaneously. The results showed that the average modeling fitting error is 8.62%. The background value coefficients of the MGM(1,n) model were optimized, the average modeling fitting error was reduced to 1.01%, and the average prediction error was reduced to 1.33%. Combining the fractional optimization with the background-value coefficient optimization, the optimal background-value coefficient α and the order r were selected. The results showed that the average modeling fitting error is 0.85%, and the average prediction error is 0.38%. The results demonstrate that the grey system theory can effectively predict the diameter of polyacrylonitrile electrospinning fibers with high prediction accuracy. This theory can increase the control of nanofiber diameters in production.

scholarly journals An Improved Nonequidistant Grey Model Based on Simpson Formula and Its Application

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhiming Hu ◽  
Chong Liu
Keyword(s):  
Prediction Accuracy ◽  
Prediction Models ◽  
Prediction Performance ◽  
Grey System Theory ◽  
Grey Model ◽  
Grey Prediction ◽  
Grey System ◽  
Proposed Model ◽  
Grey Models ◽  
High Prediction

Grey prediction models have been widely used in various fields of society due to their high prediction accuracy; accordingly, there exists a vast majority of grey models for equidistant sequences; however, limited research is focusing on nonequidistant sequence. The development of nonequidistant grey prediction models is very slow due to their complex modeling mechanism. In order to further expand the grey system theory, a new nonequidistant grey prediction model is established in this paper. To further improve the prediction accuracy of the NEGM (1, 1, t2) model, the background values of the improved nonequidistant grey model are optimized based on Simpson formula, which is abbreviated as INEGM (1, 1, t2). Meanwhile, to verify the validity of the proposed model, this model is applied in two real-world cases in comparison with three other benchmark models, and the modeling results are evaluated through several commonly used indicators. The results of two cases show that the INEGM (1, 1, t2) model has the best prediction performance among these competitive models.

scholarly journals Time-Delayed Polynomial Grey System Model with the Fractional Order Accumulation

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Lin Chen ◽  
Zhibin Liu ◽  
Nannan Ma
Keyword(s):  
System Theory ◽  
Fractional Order ◽  
Grey System Theory ◽  
Small Sample ◽  
System Model ◽  
Grey Prediction ◽  
Grey System ◽  
Forecasting Models ◽  
New Information ◽  
Grey System Model

In this work, a novel time-delayed polynomial grey prediction model with the fractional order accumulation is put forward, which is abbreviated as TDPFOGM(1,1), based on the new grey system theory to predict the small sample in comparison with the existing forecasting models. The new model takes into account the nonhomogeneous term and the priority of new information can be better reflected in the in-sample model. The data in this paper all come from the existing literatures. The results demonstrate that the TDPFOGM(1,1) model outperforms the TDPGM(1,1) and FOGM(1,1) model.

Study and Application of Grey System Theory for Fatigue Life Prediction

2009 ◽  
Vol 413-414 ◽  
pp. 717-724 ◽  
Author(s):  
Xu Liang Wang ◽  
Hong Nie ◽  
Quan Jiang
Keyword(s):  
System Theory ◽  
Fatigue Life ◽  
Prediction Error ◽  
Life Prediction ◽  
Grey System Theory ◽  
Fatigue Life Prediction ◽  
New Method ◽  
Grey Model ◽  
Grey System ◽  
Safe Side

The complexity of fatigue phenomenon results in the difficulty of fatigue life prediction. The fatigue phenomenon is treated as being in a grey system. By using of grey system theory, a new method for fatigue life prediction based on grey model GM(1,1) is proposed. Results of the example show that the prediction error can be reduced from 61.55% to 24.32%, and the prediction is on the safe side. Therefore, it is proved that the method is practical and useful for fatigue life prediction in engineering.

Application of Grey Model Theory to Forecast Flight Training Time

2014 ◽  
Vol 635-637 ◽  
pp. 1696-1699
Author(s):  
Jun Wen ◽  
Hai Feng Duan ◽  
Shu Xia Sun ◽  
Ming Xing Li ◽  
Jian Fei Lv
Keyword(s):  
System Theory ◽  
Grey System Theory ◽  
Grey Model ◽  
Flight Training ◽  
Civil Aviation ◽  
Grey System ◽  
Short Term ◽  
Training Time ◽  
Forecast Result ◽  
High Prediction

Grey model GM(1,1) is applied to forecast flight training time. The discreteness of originality data is overcome and the high-precise predicted result is received under the condition of a small amount of data. This paper takes a short-term forecast flight training time of Civil Aviation Flight University of China (CAFUC) by using grey system theory. With a comparison of the actual data to the forecast result, it is proved that using grey system theory to forecast the flight training time of Civil Aviation Flight University of China is feasible with relatively high prediction accuracy.

Research on Intelligent Decision Method Based on Grey System Theory in the Prediction of Grain Yield in Jilin Province

2014 ◽  
Vol 519-520 ◽  
pp. 775-779
Author(s):  
Xing Mei Xu
Keyword(s):  
Grain Yield ◽  
Prediction Error ◽  
Grey System Theory ◽  
Jilin Province ◽  
Grey System ◽  
Yield Data ◽  
Prediction Ability ◽  
Average Prediction Error ◽  
Intelligent Decision ◽  
Multiple Attribute

In order to improve the prediction ability of grain yield, the grain yield data of Jilin province is taken as the research object, GM(1,1) model and GM (1,N) model is established. According to the correlation analysis results, some key correlation factors of grain yield are selected into the prediction model, including the amount of chemical fertilizer, the end head of livestock, the grain sown area etc, and carries on the forecast to the grain yield of 2010-2012. The predicted results show that the average prediction error of GM(1,1) model is 6.6705% and the average prediction error of GM (1,N) model is 5.2020%. Through the comparative analysis, GM (1,N) model has higher prediction accuracy for the multiple attribute intelligent decision problem, it can be used for the prediction of grain yield in Jilin province.

Prediction of Technical Reserves Based on Grey Model — GM(1,1): Evidence from Non-life Egyptian Insurance Market

2019 ◽  
Vol 10 (9) ◽  
pp. 852-860
Author(s):  
Mahmoud Elsayed ◽  
◽  
Amr Soliman ◽  
Keyword(s):  
System Theory ◽  
Grey System Theory ◽  
Insurance Market ◽  
Grey Model ◽  
Loss Ratio ◽  
Grey System ◽  
Mathematical Technique ◽  
Supervisory Authority ◽  
Error Ratio ◽  
Using Data

Grey system theory is a mathematical technique used to predict data with known and unknown characteristics. The aim of our research is to forecast the future amount of technical reserves (outstanding claims reserve, loss ratio fluctuations reserve and unearned premiums reserve) up to 2029/2030. This study applies the Grey Model GM(1,1) using data obtained from the Egyptian Financial Supervisory Authority (EFSA) over the period from 2005/2006 to 2015/2016 for non-life Egyptian insurance market. We found that the predicted amounts of outstanding claims reserve and loss ratio fluctuations reserve are highly significant than the unearned premiums reserve according to the value of Posterior Error Ratio (PER).

Application of Grey System Theory to tree growth prediction

2000 ◽  
Vol 11 (1) ◽  
pp. 34-36 ◽  
Author(s):  
Wang Jing ◽  
Hou Yuesong ◽  
Li Weilin ◽  
Cheng Wenhui
Keyword(s):  
System Theory ◽  
Tree Growth ◽  
Grey System Theory ◽  
Grey System ◽  
Growth Prediction

Grey-based model for forecasting Mauritius international tourism from different regions

2017 ◽  
Vol 7 (2) ◽  
pp. 259-271 ◽  
Author(s):  
Medha Pirthee
Keyword(s):  
System Theory ◽  
Grey System Theory ◽  
International Tourism ◽  
Grey System ◽  
Private Companies ◽  
Content Type ◽  
System Models ◽  
Economic Decision Making ◽  
Tourism Sector ◽  
The Government

Purpose The purpose of this paper is to understand the trend and forecast the number of tourists from different regions of the world to Mauritius. Design/methodology/approach The paper adopts two grey system models, the even model GM(1,1) and the non-homogeneous discrete grey model (NDGM), to forecast the total number of international tourism to Mauritius and its structure from different regions tourist arrivals to Mauritius for the next three years. Grey system theory models were used to account for uncertainties and the dynamism of the tourism sector environment. The two models were applied as a comparison to obtain more reliable forecasting figures. Findings The results demonstrate that both of the grey system models can be successfully applied with high accuracy for Mauritian tourism prediction, and also the number of tourist arrivals to Mauritius shows a continued augmentation for the upcoming years. Practical implications Forecasting is meaningful since the Government of Mauritius, private companies or any concerned authority can adopt the forecasting methods exposed in this paper for the development of the tourism sector through managerial and economic decision making. Originality/value Mauritius is a charming travel destination. Through this paper, it can be seen that future tourism travel to Mauritius has been successfully predicted based on previous data. Moreover, it seems that the grey system theory models have not been utilised yet as forecasting tools for the tourism sector of Mauritius as opposed to other countries such as China and Taiwan.

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