Grey System Model Research Articles

The GM Model of Application and Inspection in Harbin Local Tax Revenue Forecasts

GM (n, h) model is the model of grey system modeling, which uses n-order differential equations of h variables. N and h in different models have different meanings and uses[1].Use of model GM(1,1) and model GM(1,h) to tax revenues of the grey system model is established, and applied to the Harbin local tax revenue and the main tax prediction and verification, which provide intellectual support for the local government decision-making is very important [2].At first, this paper forecast the Harbin local tax amount, then respectively to forecast the main tax and inspection (value added tax, business tax, enterprise income tax, individual income tax),Through to the amount of tax and the main tax predicted results of the inspection, show that the prediction error is not big, in line with the actual.

Dimensional Upgrade Approach for Spatial-Temporal Fusion of Trend Series in Subsidence Evaluation

Physical models and grey system models (GSMs) are commonly used to evaluate and predict physical behavior. A physical model avoids the incorrect trend series of a GSM, whereas a GSM avoids the assumptions and uncertainty of a physical model. A technique that combines the results of physical models and GSMs would make prediction more reasonable and reliable. This study proposes a fusion method for combining two trend series, calculated using two one-dimensional models, respectively, that uses a slope criterion and a distance weighting factor in the temporal and spatial domains. The independent one-dimensional evaluations are upgraded to a spatially and temporally connected two-dimensional distribution. The proposed technique was applied to a subsidence problem in Jhuoshuei River Alluvial Fan, Taiwan. The fusion results show dramatic decreases of subsidence quantity and rate compared to those estimated by the GSM. The subsidence behavior estimated using the proposed method is physically reasonable due to a convergent trend of subsidence under the assumption of constant discharge of groundwater. The technique proposed in this study can be used in fields that require a combination of two trend series from physical and nonphysical models.

Research on Safety Levels in the Chinese Construction Materials Industry Based on Gray Model Forecasting

This paper illustrated the Safety levels in the Chinese Construction Materials Industry (CMMI) during the Twelfth Five-Year Plan. By establishing gray system model, the index of CCMI, such as supply, demand, import, export, and so on, were forecasted. Based on the Index system of CCMI safety that is used to investigate the safety levels, these predicted data were analyzed. Our results show that there is a danger that CCMI safety will be reduced during the Twelfth Five-Year Plan, mainly because of overdependence on exports and lack of international competitiveness. Finally, the paper discusses how to improve CCMI safety during the Twelfth Five-Year Plan.

Combining gray system and poroelastic models to investigate subsidence problems in Tainan, Taiwan

Tainan, located in southwestern Taiwan, is a high-risk region for flooding and climate change effect and has a potential for future heavy rains. Groundwater pumping for aquaculture and irrigation along the coastal plain of Tainan is monitored due to subsidence. Predicting future subsidence and understanding the effect of climate change on subsidence can assist with regard to the planning and management of water and land resources in the early stages of subsidence, whose possible damage can thus be avoided. This study combines a physical model, called the nonlinear poroelastic model, and a gray-box model, called the gray system model, to evaluate and predict subsidence in the Tainan area. The subsidence under the climate change effect caused by changes in groundwater use is estimated. The climate change impact on subsidence and the potential for subsidence hazards are also analyzed. When taking into consideration the climate change effect, it is predicted that subsidence in Tainan will increase with increasing groundwater use. The maximum subsidence will increase from 29 cm (without additional discharge) to 34 cm (with additional 23.6 % discharge) and 35 cm (with additional 31.7 % discharge) in Xiaying District by 2039. The pattern of subsidence areas obtained by combining the nonlinear poroelastic and the gray system models is similar to that obtained using only the gray system model. However, the largest subsidence areas are different, and the subsidence quantity is much lower for the former due to the constraint of the physics-based poroelastic model. The combination of the nonlinear poroelastic model and the gray system model gives more reasonable estimations than either model can give alone. Large subsidence impact areas are located in the northwestern part of Tainan, and the largest impact is in Xiaying District. The potential for subsidence hazards is classified from the subsidence rates. The subsidence rate in Tainan is below 3 cm/year in the target years, and thus the potential for subsidence hazards is at middle and low levels. With climate change, the area with a middle level of potential for subsidence hazards extends across Yanshui, Xiaying, and Beimen Districts. From the study results, although the subsidence quantity is relatively low, rising sea levels and other effects of climate change expose Tainan to a high risk of flooding. Land and water resources should thus be managed to alleviate subsidence problems in the future.

Estimating civil aircraft’s research and manufacture cost by using grey system model and neural network algorithm

Purpose – The purpose of this paper is to propose novel civil aircraft cost parameters’ selection method and novel cost estimation approach for civil aircraft so as to effectively simulate or forecast civil aircraft cost under poor information and small sample. Design/methodology/approach – Based on existent cost estimation indexes, this paper summarized civil aircraft research and manufacturing cost impact index system and adopted grey relational model to select most important impact factors. Consider civil aircrafts’ cost information could not be easily collected, the author must estimate their costs with limited sample and poor information. A combination model of GM (0, N) model and BP neural network algorithm is proposed. Both advantages of simulation of BP neural network algorithm and poor information generation of GM (0, N) were effectively combined. Then steps of combined model were given out. Finally, nine types of aircrafts were used to test the validity of proposed model. As comparing with the traditional multiple linear regression model and simple GM (0, N) model, results indicated that proposed model can do the work better. Findings – Grey relational model can be applied for parameters’ selection and combined GM (0, N) model and BP neural network algorithm can estimate aircraft’s cost as well. Results show that novel combined model could get high forecasting accuracy. Practical implications – Cost estimation is key problem in production management of civil aircraft. Effective cost management could promote competitiveness of aircraft manufacturing company. Proposed combined model can be applied for civil aircraft cost estimation. Similarly, it could be applied for other complex equipment cost estimation. Originality/value – The paper succeeds in proposing grey relational model for cost parameters’ selection and constructing a combination model of GM (0, N) model and BP neural network algorithm. Algorithm of the proposed model was discussed and steps were given out.

Using a Novel Grey System Model to Forecast Natural Gas Consumption in China

Accurate prediction of the future energy needs is crucial for energy management. This work presents a novel grey forecasting model that integrates the principle of new information priority into accumulated generation. This grey model can better reflect the priority of the new information theoretically. The results of two practical examples demonstrate that this grey model provides very remarkable short-term predication performance compared with traditional grey forecasting model for limited data set forecasting. It is applied to Chinese gas consumption forecasting to show its superiority and applicability.

Properties of the GM(1, 1) with fractional order accumulation

For traditional grey model (GM(1,1)), it is proved theoretically that the initial condition is not utilized, and the simulative value is convex and increased or is the decreased and concave when the actual sequence is nonnegative increased. These shortcomings are the results of traditional first order accumulation on grey system model. However, for the GM(1,1) with fractional order accumulation, the initial condition is utilized, and the monotonicity and convexity of simulative value are uncertain when the actual value is nonnegative increased. The results of practical numerical examples demonstrate that the GM(1,1) with fractional order accumulation provides very remarkable predication performance compared with the traditional GM(1,1) model.

Research on Grey System Model and Its Application on Displacement Prediction in Tunnel Surrounding Rock

In the process of the highway tunnel construction, the stability and the reliability of tunnel rock is an important guarantee to ensure safety construction. Tunnel surrounding rock deformation monitoring is an important method for obtaining information on surrounding rock and controlling tunnel stability in the period of construction. Forecasting deformation of surrounding rock is the key to estimate shoring types, parameter and longtime stability after being commissioned for use. Considering the non-linear characteristic of deformation of the tunnel, the grey system prediction models were proposed. Based on the displacement of Guankouya rock tunnel, grey models of the GM(1, 1) and the Model DGM(2, 1) were established for the tunnel grey forecasting model of the rock tunnel displacement. The calculated results show that two models for tunnel displacement generally were predictable. GM(1, 1) and DGM(2, 1) models are similar to the tunnel displacement development model. Application examples demonstrate that it has extraordinary adaptability to the tunnel displacement forecast and all types of surrounding rock displacement can be predicted better by the grey model and the model has high simulation and prediction accuracy.

Dynamic Correlation Effects between Marine Economic Development and the Comprehensive Competitiveness of the Coastal Zone

Study on the rationality of marine economy and coastal comprehensive competitiveness, is one of the fundamental theoretical problems co-ordination of land and sea. In order to evaluate the comprehensive competitiveness of coastal areas based on gray system model, calculated correlation and trends between economic development and integrated coastal ocean competitiveness. The result shows that there is a strong correlation between marine economy and inland comprehensive competitiveness in China. And the correlation presents an increasing trend. Marine economy is highly depending on inland economy social development. To achieve sustainable development, extended to the advantage of relying on the sea should be given full play to realize interactive development between marine system and inland system. The conclusions could provide a reference for the policies and plans to develop coastal economic development and marine economic.

IUbiq-Lys: prediction of lysine ubiquitination sites in proteins by extracting sequence evolution information via a gray system model

As one of the most important posttranslational modifications (PTMs), ubiquitination plays an important role in regulating varieties of biological processes, such as signal transduction, cell division, apoptosis, and immune response. Ubiquitination is also named “lysine ubiquitination” because it occurs when an ubiquitin is covalently attached to lysine (K) residues of targeting proteins. Given an uncharacterized protein sequence that contains many lysine residues, which one of them is the ubiquitination site, and which one is of non-ubiquitination site? With the avalanche of protein sequences generated in the postgenomic age, it is highly desired for both basic research and drug development to develop an automated method for rapidly and accurately annotating the ubiquitination sites in proteins. In view of this, a new predictor called “iUbiq-Lys” was developed based on the evolutionary information, gray system model, as well as the general form of pseudo-amino acid composition. It was demonstrated via the rigorous cross-validations that the new predictor remarkably outperformed all its counterparts. As a web-server, iUbiq-Lys is accessible to the public at http://www.jci-bioinfo.cn/iUbiq-Lys. For the convenience of most experimental scientists, we have further provided a protocol of step-by-step guide, by which users can easily get their desired results without the need to follow the complicated mathematics that were presented in this paper just for the integrity of its development process.

Long-term Clock Bias Prediction Based on An ARMA Model

The long-term and reliable prediction of satellite clock bias (SCB) is an important prerequisite for realizing the satellite autonomous navigation and orbit determination. Considering the shortcomings of the quadratic polynomial model (PM) and gray system model (GM) in the long-term prediction of SCB, a new prediction method of SCB based on an ARMA (Auto-Regressive Moving Average) model is proposed to represent the variation characteristics of SCB more accurately. In this paper, a careful precision analysis of the 90-day SCB prediction is made to verify the feasibility and validity of this proposed method by using the IGS (International GNSS Service) clock data. According to the variation characteristics of each satellite clock, the pattern recognition, modeling and prediction of SCB are conducted, and the detailed comparison is made with the other three models at the same time. The results show that adopting the ARMA model can effectively improve the accuracy of long-term SCB prediction.

The Non-equidistant Multivariable New Information MGM(1,n) Based on New Information Background Value Constructing

Applying the principle in which new information should be used fully and modeling method of grey system for the problem of lower precision as well as lower adaptability in non-equidistant multivariable MGM(1,n)model, a non-equidistant multivariable new information MGM(1,n) model was put forward which was taken the mth component as the initialization. As the background value is an important factor affecting the precision of grey system model, based on index characteristic of grey model, the characteristic of integral and new information principle, the new information background value in non-equidistant multivariable new information MGM(1,n) was researched and the discrete function with non-homogeneous exponential law was used to fit the accumulated sequence and the formula of new information background value was given. This new non-equidistant multivariable MGM(1,n) model can be used in equidistance & non-equidistance modeling and has the characteristic of high precision as well as high adaptability. Examples validate the practicability and reliability of the proposed mode l . DOI : http://dx.doi.org/10.11591/telkomnika.v12i6.4822

Application of a Novel Data Mining Method Based on Wavelet Analysis and Chaotic Neural Network on Satellite Clock Bias Prediction

A novel four-stage data mining method for clock bias prediction based on wavelet analysis and chaotic neural networks is proposed. The basic ideas, prediction models and steps of clock bias prediction based on wavelet analysis and chaotic neural network are discussed respectively. And then, to validate the feasibility and validity of the proposed method, make a careful precision analysis for satellite clock bias prediction with the performance parameters of GPS satellite clock, and make comparison and analysis with Grey system model and neural network model. The results of simulation shows that the prediction precision of the novel four-stage model based on wavelet analysis and chaotic neural networks is more better, can afford high precise satellite clock bias prediction for real-time GPS precise point positioning.

Hybrid Control of a Wind Induction Generator Based on Grey–Elman Neural Network

This brief presents the design of an optimal wind energy control system for maximum power point tracking. With the help of a grey predictor for the preprocessor, a high-performance online training Elman neural network (ENN) is designed to derive the turbine speed needed to extract maximum power from wind. Moreover, the connective weights of the improved ENN are trained online by the backpropagation learning algorithm. Compared to earlier methods, better results are obtained when the ENN controller is used together with the grey system modeling approach. Performance of the proposed approach is verified by the experimental results.

Mathematical Models for Predicting and Managing Water Resources — The Case of China in 2025

An effective management of water supply is critically significant to a countrys water utilities, and accurate prediction of water supply and demand is of key importance for water supply management. The objectives of this paper are to use Grey System Model (GSM) and Linear Regression Model to forecast the water demand and water supply respectively in China 2025, and then propose a new Optimal Allocation Model (OAM) to generate solution so that analysts and decision makers can gain insight and understanding. The two predictive models take into account four major factors including domestic development, agriculture, industries and eco-environment, calculating a deficit between water demand and water supply in China 2025. Then the OAM, which considers desalinization, irrigation saving and urban recycling, provides a feasible solution to fill the gap and an effectual management of water supply.

Assessment of plain river ecosystem function based on improved gray system model and analytic hierarchy process for the Fuyang River, Haihe River Basin, China

A comprehensive conceptual model for assessment of river ecosystem function and a multilevel indicator system by including the hydrology and water resources, water environment, physical habitat, as well as biotic structure for the plain river ecosystem function of the Haihe River Basin were constructed in this research. The indicator weights were determined by analytic hierarchy process and the improved gray system model was used to assess the ecosystem function of the Fuyang River in the Haihe River Basin. The elements that determine the plain river ecosystem function are physical habitat, hydrology and water resources, biotic structure and water environment. The maximum membership degree of gray incidence of ecosystem function for the Fuyang River is 0.7228, indicating that ecosystem function of the Fuyang River is at a critical state; comprehensive restoration including water quantity recovery and water quality improvement should be introduced to improve the ecosystem function of the Fuyang River.

The Application of Grey Prediction on the Fast Control Power Supply in EAST

Based on the grey theory, a novel model is built to predict the input signal of fast control power supply used in Experimental Advanced Superconducting Tokamak (EAST). The model can be used as online metabolic grey filtering and one-step prediction of different input signals. Results of simulation and experiment show that the predicting algorithm based on the grey system model can predict the input signal primarily.

A New Hybrid Grey Neural Network Based on Grey Verhulst Model and BP Neural Network for Time Series Forecasting

The advantages and disadvantages of BP neural network and grey Verhulst model for time series prediction are analyzed respectively, this article proposes a new time series forecasting model for the time series growth in S-type or growth being saturated. From the data fitting's viewpoint, the new model named grey Verhulst neural network is established based on grey Verhulst model and BP neural network. Firstly, the Verhulst model is mapped to a BP neural network, the corresponding relationships between grey Verhulst model parameters and BP network weights is established. Then, the BP neural network is trained by means of BP algorithm, when the BP network convergences, the optimized weights can be extracted, and the optimized grey Verhulst neural network model can be obtained. The experiment results show that the new model is effective with the advantages of high precision, less samples required and simple calculation, which makes full use of the similarities and complementarities between grey system model and BP neural network to settle the disadvantage of applying grey model and neural network separately. It is concluded that grey Verhulst neural network is a feasible and effective modeling method for the time series increasing in the curve with S-type.

The Forecast of Transportation Flows under Certain and Uncertain Trends: The Application of Combined Grey System Models

The complexity of modern economic system makes the forecast about correlated variable more difficult. Combined grey system models with general grey forecasting method and grey verhulst forecasting method are utilized to overcome the forecast issues under certain and uncertain changing trends. The results indicate that the forecast accuracy is high and short-term forecasts are available, which supports the methodological applicability and robustness.

On computational algorithms of grey numbers based on information background

PurposeThe purpose of this paper is to redefine the concept of grey number with information background and to propose novel grey number algorithms.Design/methodology/approachAs the basic element of a grey system, grey number is defined as a number of which the true value is unknown because of the limited information while the boundary or possible value set can be known. Grey number algorithms are key points for constructing grey system models and helping to calculate model results. The concept of grey number is redefined with information background in this paper. Novel grey number algorithms are defined considering all the different forms of grey number information background. Several illustrative examples are applied to state the process of the proposed algorithms.FindingsThe results show that the novel grey number algorithms can do the operations well no matter what the grey number's type. It is useful to create effective grey system models and to do system analysis, forecasting, decision making and control in grey systems.Practical implicationsThe method exposed in the paper can be used to calculate different grey numbers and used in constructing new grey models.Originality/valueThe paper succeeds in defining new grey number algorithm. The concept and its remarks of grey number are discussed. The novel algorithm can be used in creating new grey models and solve the modelling parameters in of grey systems theory undoubtedly.