scholarly journals Two Novel Grey System Models and Their Applications on Landslide Forecasting

2016 ◽  
Vol 2016 ◽  
pp. 1-6
Author(s):  
De-Yi Ma ◽  
Jian-Lin Li
Keyword(s):  
Original Data ◽  
Small Sample ◽  
Grey Model ◽  
The Novel ◽  
Grey System ◽  
Numerical Examples ◽  
System Models ◽  
Forecasting Models ◽  
Landslide Forecasting

For the small sample poor information, grey model is one of the good forecasting models. However, the simulation curve of original data is not consistent with that of the data by translations. In this paper, we present two novel grey system models, that is, generalized grey model and generalized discrete grey model. Compared with grey model, we prove that the simulation curve of original data is consistent with that of the new data by translations for the novel grey model, which was also demonstrated by the results of practical numerical examples.

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.

GSM(1) Model and its Application

2011 ◽  
Vol 225-226 ◽  
pp. 1360-1363
Author(s):  
Bin Zeng ◽  
Wu Jun Zeng
Keyword(s):  
System Application ◽  
Original Data ◽  
Grey Model ◽  
Time Interval ◽  
Difference Quotient ◽  
Grey System ◽  
Effective Form ◽  
Adaptive Capability ◽  
The Right

Although the GM(1,1) model has been successfully adopted in various fields and has been demonstrated promising prospect. But the form of the GM(1,1) model is single and obviously is not suitable for all data with different character. In order to increase the adaptive capability some different forms of the grey model is needed to be established. The paper adopts combinatorial form instead of const in the right part of the grey equation which we call it GSM(1) model. GSM(1) model is one variable index serials function which contains more information in the equation and can find more complicated law between data. On the condition of the original data multiplying the time interval the paper introduces difference quotient into the equation and establishes the unequal GSM(1) model. The examples prove GSM(1) is an effective form to improve the accuracy of the grey model. It provides a new way for the grey system application.

Combined forecasting of regional logistics demand optimized by genetic algorithm

2014 ◽  
Vol 4 (2) ◽  
pp. 221-231 ◽  
Author(s):  
Feng-biao He ◽  
Jun Chang
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Original Data ◽  
Forecasting Model ◽  
Grey System ◽  
Content Type ◽  
Regional Logistics ◽  
Forecasting Models ◽  
Logistics Demand ◽  
Combined Forecasting

Purpose – The purpose of this paper is to establish a combined forecasting model to predict regional logistics demand, which is an important procedure on decision making of regional logistics planning. Design/methodology/approach – There are several kinds of mathematical models often used in forecasting regional logistics demand. Trend extrapolation method extrapolates the future development trends bases on the hypothesis that the regional logistics will develop steadily. Grey system method predicts the change of logistics demand by the generation and development of original data sequence and excavation of inherent rules of the original data. Regression method obtains the change rules through the analysis between explained variable and explanatory variables. Each method has unique characteristics. In order to improve the accuracy of the prediction, combined methods are established. Genetic algorithm is used to determine the weights of different single models. Findings – The results show that the combined forecasting model optimised by genetic algorithm can improve the accuracy. Practical implications – Combined forecasting model can integrate the advantages of different single forecasting models. The key of improving the accuracy is to determine the weights of single forecasting models. Genetic algorithm can do well in finding suitable weights of each single forecasting model. Originality/value – The paper succeeds in providing a combined forecasting model using genetic algorithm to determine the weights of each single prediction model, which helps to the decision making of regional logistics demand.

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).

scholarly journals Utilizing an Adaptive Grey Model for Short-Term Time Series Forecasting: A Case Study of Wafer-Level Packaging

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Che-Jung Chang ◽  
Der-Chiang Li ◽  
Wen-Li Dai ◽  
Chien-Chih Chen
Keyword(s):  
Semiconductor Manufacturing ◽  
Manufacturing System ◽  
Grey System Theory ◽  
Grey Model ◽  
Grey System ◽  
Wafer Level ◽  
Short Term ◽  
Packaging Process ◽  
Wafer Level Packaging

The wafer-level packaging process is an important technology used in semiconductor manufacturing, and how to effectively control this manufacturing system is thus an important issue for packaging firms. One way to aid in this process is to use a forecasting tool. However, the number of observations collected in the early stages of this process is usually too few to use with traditional forecasting techniques, and thus inaccurate results are obtained. One potential solution to this problem is the use of grey system theory, with its feature of small dataset modeling. This study thus uses the AGM(1,1) grey model to solve the problem of forecasting in the pilot run stage of the packaging process. The experimental results show that the grey approach is an appropriate and effective forecasting tool for use with small datasets and that it can be applied to improve the wafer-level packaging process.

Kernel Null Space Marginal Fisher Analysis for Face Recognition

2014 ◽  
Vol 889-890 ◽  
pp. 1065-1068
Author(s):  
Yu’e Lin ◽  
Xing Zhu Liang ◽  
Hua Ping Zhou
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Sample Size ◽  
High Performance ◽  
Null Space ◽  
Small Sample Size ◽  
Recognition Rate ◽  
Original Data ◽  
Small Sample ◽  
Neighborhood Structure

In the recent years, the feature extraction algorithms based on manifold learning, which attempt to project the original data into a lower dimensional feature space by preserving the local neighborhood structure, have drawn much attention. Among them, the Marginal Fisher Analysis (MFA) achieved high performance for face recognition. However, MFA suffers from the small sample size problems and is still a linear technique. This paper develops a new nonlinear feature extraction algorithm, called Kernel Null Space Marginal Fisher Analysis (KNSMFA). KNSMFA based on a new optimization criterion is presented, which means that all the discriminant vectors can be calculated in the null space of the within-class scatter. KNSMFA not only exploits the nonlinear features but also overcomes the small sample size problems. Experimental results on ORL database indicate that the proposed method achieves higher recognition rate than the MFA method and some existing kernel feature extraction algorithms.

scholarly journals Generalized fractional grey system models: The memory effects perspective

2021 ◽  
Author(s):  
Wanli Xie ◽  
Wen-Ze Wu ◽  
Chong Liu ◽  
Mark Goh
Keyword(s):  
Memory Effects ◽  
Grey System ◽  
System Models

A novel fractional discrete grey model with an adaptive structure and its application in electricity consumption prediction

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yitong Liu ◽  
Yang Yang ◽  
Dingyu Xue ◽  
Feng Pan
Keyword(s):  
Fractional Order ◽  
Electricity Consumption ◽  
Grey Model ◽  
The Novel ◽  
Raw Data ◽  
Content Type ◽  
Power Load ◽  
Grey Models ◽  
Novel Model ◽  
Consumption Prediction

PurposeElectricity consumption prediction has been an important topic for its significant impact on electric policies. Due to various uncertain factors, the growth trends of electricity consumption in different cases are variable. However, the traditional grey model is based on a fixed structure which sometimes cannot match the trend of raw data. Consequently, the predictive accuracy is variable as cases change. To improve the model's adaptability and forecasting ability, a novel fractional discrete grey model with variable structure is proposed in this paper.Design/methodology/approachThe novel model can be regarded as a homogenous or non-homogenous exponent predicting model by changing the structure. And it selects the appropriate structure depending on the characteristics of raw data. The introduction of fractional accumulation enhances the predicting ability of the novel model. And the relative fractional order r is calculated by the numerical iterative algorithm which is simple but effective.FindingsTwo cases of power load and electricity consumption in Jiangsu and Fujian are applied to assess the predicting accuracy of the novel grey model. Four widely-used grey models, three classical statistical models and the multi-layer artificial neural network model are taken into comparison. The results demonstrate that the novel grey model performs well in all cases, and is superior to the comparative eight models.Originality/valueA fractional-order discrete grey model with an adaptable structure is proposed to solve the conflict between traditional grey models' fixed structures and variable development trends of raw data. In applications, the novel model has satisfied adaptability and predicting accuracy.

scholarly journals A Study on the Generalized Approximation Modeling Method Based on Fitting Sensitivity for Prediction of Engine Performance

2017 ◽  
Vol 2017 ◽  
pp. 1-12
Author(s):  
Lin Lin ◽  
Fang Wang ◽  
Shisheng Zhong
Keyword(s):  
Engine Performance ◽  
Grey Model ◽  
Model Parameters ◽  
Bayesian Regularization ◽  
Safety Engineering ◽  
The Novel ◽  
Mean Square ◽  
Approximation Model ◽  
Mean Square Errors ◽  
Fitting Accuracy

Prediction technology for aeroengine performance is significantly important in operational maintenance and safety engineering. In the prediction of engine performance, to address overfitting and underfitting problems with the approximation modeling technique, we derived a generalized approximation model that could be used to adjust fitting precision. Approximation precision was combined with fitting sensitivity to allow the model to obtain excellent fitting accuracy and generalization performance. Taking the Grey model (GM) as an example, we discussed the modeling approach of the novel GM based on fitting sensitivity, analyzed the setting methods and optimization range of model parameters, and solved the model by using a genetic algorithm. By investigating the effect of every model parameter on the prediction precision in experiments, we summarized the change regularities of the root-mean-square errors (RMSEs) varying with the model parameters in novel GM. Also, by analyzing the novel ANN and ANN with Bayesian regularization, it is concluded that the generalized approximation model based on fitting sensitivity can achieve a reasonable fitting degree and generalization ability.

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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