Grey GM Research Articles

Using a set of GM(1,1) models to predict values of diagnostic symptoms

The main purpose of this study is to develop a methodology of predicting values of vibration symptoms of fan mills in a combined heat and power (CHP) plant. The study was based on grey system theory and GM(1,1) prognostic models with different window sizes for estimating model parameters. Such models have a number of features that are desirable from the point of view of data characteristics collected by the diagnostic system. When using moving window, GM(1,1) models tend to be adaptive. However, selecting an inappropriate window size can result in excessive forecast errors. The present study proposes three possible methods that can be used in automated diagnostic systems to counteract the excessive increase in the forecast error. A comparative analysis of their performance was conducted using data from fan mills in order to select the method which minimises the forecast error.

Power Load Forecasting Using Improved Grey-Markov Method

Power load forecasting is an important part of management modernization of power system. Accurate load forecasting can provide reliable guidance for grid operation and power construction planning. For load forecasting "small sample", "poor information", "uncertain", "non-linear" and other features, In this paper, GM (1.1) model was improved in the gray system theory, by constructing the background value sequence to transform the original data, using gray rolling GM (1.1) model and combining Markov prediction model to make power load forecasting. The application results show that this method is accurate and practicable.

Multi-phase information aggregation and dynamic synthetic evaluation based on grey inspiriting control lines

Purpose – The purpose of this paper is to put forward a multi-stage information aggregation method based on grey inspiriting control lines to evaluate the objects dynamically and comprehensively. Design/methodology/approach – According to the evaluation value of the objects, the positive and negative incentive lines were set up and the predicted values were solved based on the grey GM(1, 1) model, so the value with expected information could be evaluated. In the evaluation, the part above the positive incentive line should be “rewarded” and that below the negative incentive line should be “punished” appropriately. Thereby the double incentive effects of “the current development situation and future development trend” to objects could be implemented on the basis of control. Findings – This method can primarily describe the decision maker's expectancy of the development of evaluation objects and make the evaluation results have better practical application value. Research limitations/implications – Many comprehensive evaluations were always based on the past information. However, the future development trend of the evaluated object is also very important. This study can be used in the evaluation for future application and development. Originality/value – The paper succeeds in providing not only a method of multi-phase information aggregation with expectancy information, but also a simple and convenient method solving nonlinear inspiring lines objectively.

Grey Prediction of Economy Based on Improved GM (1, 1) Model

According to the characters of few economic forecasting data and complicated action mechanism, this paper makes use of the time sequence prediction theory of improved grey GM (1, 1) model to predict China’s economic contraction years, overheating years and economic cycle, and analyses the important function of grey prediction model in the economic forecasts.

Load Forecasting of Zhejiang Province Based on Combined Optimization Methods

On the basis of analysis in Zhejiang, this paper uses the trend fitting method, the quadratic exponential smoothing model and multiple linear regression and grey GM (1, 1) portfolio model to forecast electricity consumption in 2012-2020 in Zhejiang, and compare the various methods of prediction accuracy.

Tourism Flows Prediction based on an Improved Grey GM(1,1) Model

This study analyzes the factors affecting the tourist flow. These factors include tourism resources, traffic conditions and so on. In recent years, the grey forecasting model has achieved good prediction accuracy with limited data and has been widely used in various research fields. However, the grey forecasting model still have some potential problems that need to be improved, such as applicate range and prediction accuracy. It is found that original data and background value are main factors affecting the accuracy of the proposed model's application. To solve these problems, this study develops a optimization model for the GM(1,1) model problem which includes optimization of initial and background values. In order to reduce errors caused by back-ground values, the “new information prior using” principle is followed, and a liner function is dopted in the construe of background. Numerical examples verified that the simulation and prediction accuracy of the short-term forcasts is significantly increased. As a result, the newly improved model yields a high prediction capability.

Research of EDM Titanium Alloy TC11 Based on GM(1,N) Model and BP Neural Network

In order to predict the influences of electrical discharge machining (EDM) and enhance material removal rate (MRR). A model was presented for predictions of MRR in EDM TC11 test piece based on the construction method of gray GM(1,N) model and BP neural network model (GNNM).The results show that the GNNM is credible with the maximum absolute error 3.58%, the minimum absolute error 0.01% and the mean error 0.53%were obtained .The results indicated that the model can reflect the technological law of EDM TC11 and successfully predict its processing speed .The paper provides references and basis for selecting processing parameters of EDM TC11 and has practical significance.

A GM(1,N)-based economic cybernetics model for the high-tech industries in China

Purpose – The purpose of this paper is to propose an economic cybernetics model based on the grey differential equation GM(1,N) for China's high-tech industries and provide the necessary support to assist high-tech industries management departments with their policy making. Design/methodology/approach – Based on the principle of grey differential equation GM(1,N), the grey differential equations of five high-tech industries in China are established using the net fixed assets, labor quantity and patent application quantity as cybernetics variables. After the discretization and first-order subtraction reduction to the simultaneous equation of the five grey models, a linear cybernetics model is resulted in. The structure parameters in the cybernetics system show explicit economic significance and can be identified through least square principle. At last, the actual data in 2004-2010 are introduced to empirically analyze the high-tech industrial system in China. Findings – The cybernetics system for China's high-tech industries are stable, observable, and controllable. On the whole, China's high-tech industries show higher output coefficients of the patent application quantity than those of net fixed assets and labor quantity. This suggests that China's industry development mainly depends on technological innovation rather than capital or labor inputs. It is expected that the total output value of China's high-tech industries will grow at an average annual rate of 15 percent in 2011-2015, with contributions of pharmaceuticals, aircraft and spacecraft, electronic and telecommunication equipments, computers and office equipments, medical equipments and meters by 21, 16, 13, 10, and 28 percent, respectively. In addition, pharmaceuticals, as well as medical equipments and meters, present upward proportions in the gross of Chinese high-tech industries significantly. Electronic and telecommunication equipments, plus computers and office equipments exhibit an obvious decreasing proportion. The proportion of the output value of aircraft and spacecraft is basically stable. Practical implications – Empirical analysis results are helpful for related management departments to formulate reasonable industrial policies to keep the sustained and stable development of the high-tech industries in China. Originality/value – Based on the grey differential equation GM(1,N), this research puts forward an economic cybernetics model for the high-tech industries in China. This model is applicable to the economic system with small sample data set.

Railway Freight Turnover Forecast Based on the BP Neural Network

A BP neural network model was employed to forecast the railway freight turnover. First, this paper analyses the data of railway freight turnover in China from 1998 to 2012, build a three layers BP neural network, then by training and learning, a well-trained network can be used for simulating and forecasting. Finally, predict by the Grey GM(1,1) model and well-trained BP neural network respectively, and compares the errors of two prediction model, the results show that predicting the railway freight turnover by BP neural network has higher precision.

Research on Biological Materials for the Preferred of the Chlorophyll Content Gray GM (1,1) Prediction Models Based on the Different Light

Light is one of the most important factor in the growth of plants, with the advent and application of biological materials such as different artificial LED source, the new agricultural technology has been rapid development. In this study, first established the gray GM (1,1) prediction model of the pepper seedlings chlorophyll changes under the different light and then compared of the chlorophyll models under the different light. Last the study found that different artificial LED have the different effect and the forecasting curve and prediction model under the blue is optimal for pepper seedling by comparing the chlorophyll curves and predictive models of pepper seedlings under different light, so blue light is the most suitable for the growth of pepper seedlings. The results turned out that accuracy test of the three gray prediction models can achieve the best grade, and the three gray prediction models have the good practical value. Grey prediction theory can be better applied to the study of the plants.

Variable-weight Combination Prediction of Thermal Error Modeling on CNC Machine Tools

Since the thermal error modeling of CNC machine tools has characters of small sample and discrete data, the variable-weight combined modeling method was presented by integrating time series analysis and least squares support vector machines. Taking minimum sum of error square of prediction model as the optimization criterion, optimal weights in different time were calculated. Using grey GM (1, 1) model to predict the variable weights, the prediction result of thermal error was obtained as well. Application of the grey variable-weight combined model on a five axis vertical machining center indicated that it can get higher prediction accuracy than single modeling method. Therefore online error compensation to CNC machine tools will become more effective.

Gray-Markov GM (1, 1) Prediction Model Optimal Allocation of Equipment Maintenance Personnel Based on Difference Method

Based on the situation that the statistic of the missile facilities stability was small, and the conventional big example preview method was no longer of use. So, this article used the gray GM (1, 1) model to preview a facilitys stability .The gray GM (1, 1) model doesnt need a large number of example statistics, and it is also accurate, so this model solved the problem in the analyze and preview of the stability of the missiles electronic facility system, and it has possibility to imply to engineering use.

Urgency and Necessity on Adjustment of China’s Current Agricultural Structure Based on the Stability and Developmental Trend Analyses of Pork Yield in China

With the development of economy, meat gradually plays an important role in Chinese people’s dietary pattern. As the most direct reflection of agricultural system, food system has always been the main output of agricultural system, and pork has occupied an important position in Chinese food system. We attempts to find the systematical disorder of current agricultural system by analyzing the meat output of the agricultural system. H-P Filter and Grey Prediction GM(1,1) Model was adopted to explore the inner rules between pork production and agricultural system in China. The results indicated that pork consumption ratio in Chinese urban residents’ dietary pattern constantly kept about 6%, the growth potential of grain yield is limited while the growth potential of pork yield is increased in China. By Grey Prediction GM(1,1) Model, we predicted the pork yield would reach 59.07 Mt in 2020, 110.25 Mt in 2060, 205.78 Mt in 2100, the demand of feed would reach 177.22 Mt in 2020, 330.75 Mt in 2060, 617.34 Mt in 2100. In China, agricultural system is traditional “pork-grain” mode, feed has been one of the biggest section that consumed grain. The present “pork-grain” agricultural system cannot meet the huge demand of grain from feed, adjusting the agricultural structure is imperative. Reforming the current agricultural system into grassland agricultural system which takes the beef and mutton as predominates would be a good choice for China in the future.

Prediction of Water Consumption in Hospitals Based on a Modified Grey GM (0, 1∣sin) Model of Oscillation Sequence: The Example of Wuhan City

Water shortage is one of the main factors limiting urban construction and development. Scientific forecasting of water consumption is an important approach for the rational allocation of water resources. Taking the hospitals in Wuhan City as an example and basing the analysis on the characteristics of actual water consumption, we proposed a modified grey GM (0, 1∣sin) model of oscillation sequence. Using the grey theory, the variable weight-strengthening buffer operator (VWSBO) was introduced into this model to weaken the interference of the disturbance term on the data sequence. The actual quarterly total water consumption data for hospitals in Wuhan City during the period from 2010 to 2012 were used to verify the effectiveness and practicality of this modified grey GM (0, 1∣sin) model in predicting water consumption. In terms of the model’s fitting performance, the mean absolute percentage error (MAPE) of the modified model was 3.77%, indicating a higher prediction accuracy than the traditional grey GM (0, 1∣sin) model of oscillation sequences. Therefore, the modified grey GM (0, 1∣sin) model we established in this study can provide a scientific reference for administrative departments to forecast water consumption.

The Prediction of Shadow Price Based on Grey Theory

The shadow price is based on price changes in market prices, "price estimate", which still lag behind the changes in the market price.The paper calculated shadow price sensitive to changes in resource pricesdegrees, through the gray theory GM (1,1) model forecast the market price, and speculated that the estimated price of the next period resources.

Application of Grey Theory to the Field of Economic Forecasting

The Grey model is characterized by basic mathematics and a need for few raw data, but it also lacks the flexibility to adjust the model to increase the precision for the forecasting model. This article shows two types of grey forecasting models, GM(1,1) and NGBM. The GM(1,1) shows its fitting ability to the linear raw data and NGBM is superior in fitting nonlinear raw data. In this article, two practical cases are shown to demonstrate the calculation procedures of both models. The results show they are feasible in forecasting.

基于变权-物元分析模型的老工业基地区域生态安全动态预警研究—以吉林省为例

生态安全预警是生态安全研究的重要内容，对维护区域生态安全具有重要的意义。老工业基地区域生态安全预警研究具有一定的特殊性和典型性，选取吉林省为研究区，基于压力、状态、响应（P-S-R）和生态、环境、经济、社会（E-E-E-S）框架模型构建生态安全预警指标体系，运用变权-物元分析模型对1991—2011年吉林省区域生态安全的预警等级进行了测度，并结合灰色系统GM（1，1）预测模型对2015年、2020年区域生态安全态势进行预测预警。研究结果表明：（1）1991--2011年，吉林省生态安全总体水平呈逐步上升的趋势，区域生态安全预警等级从“巨警”上升为“轻警”，指示灯由“红色预警”逐步变为“蓝色预警”；（2）2015年，吉林省的生态安全预警等级属于“轻警”，指示灯为“蓝色预警”，到2020年，吉林省的生态安全预警等级属于“无警”，指示灯为“绿色预警”，但具有向“轻警”变化的态势；（3）制约吉林省生态安全水平提升的主要因素包括单位面积耕地化肥负荷量、城镇化、人均水资源量、GDP增长率、第一产业占GDP比重等，这些因素应是今后生态环境保护调控的重点，本研究为吉林省经济一生态．社会可持续发展提供参考。

Predication Method for China's Civil Aviation Fuel Consumption

With the China’s civil aviation industry gradual market-oriented and the rapid development of China’s economy, China’s civil aviation transportation fuel consumption has grown significantly in nearly past three decades. Therefore, it’s a very important strategic significance of the prediction of China’s civil aviation transportation fuel consumption. In this paper, gray system and neural network approach, combined with China’s civil aviation industry 1980-2010 total traffic volume of the data, we establish gray system GM (1,1) model and BP neural network model for civil aviation transport volume. Training and simulation of the back propagation neutral network model and the gray system GM(1,1) used MATLAB. BP neural network modeling takes into account in three factors: the number of aircraft aviation industry, flight hours and total turnover. The fitting precision of the gray system GM(1,1) model is 64.2% while the fitting precision of the back propagation neutral network model is 90.16%. Thus, the back propagation neutral network model is better for estimating Civil aviation fuel consumption.

Grey Interval Time Series Forecast

Numerous grey forecast methods have been developed for small size time series data. These methods are aimed at forecasting single-value data. Grey methods that can forecast interval-value data are rare. Therefore, this study attempts to use the grey system theory to forecast interval-value data. This research is an innovative approach that combines grey interval numbers, traditional forecast methods and the GM(1, 1) model to develop four grey interval forecast methods. Specifically, the first three methods make use of interval grey number operations, and the last method expands the grey GM(1, 1) model. In order to compare the forecast accuracy of the four methods, this study designs two forecast error measurement indices: grey mean absolute deviation (GMAD) and grey mean absolute percent error (GMAPE); 36 sets of real-world interval data are employed to compare the forecast accuracy of the four methods. The forecast result shows the maximum and minimum of the average GMAPE for the four methods to be 1.791% and 0.622%, respectively. This result indicates that the proposed four methods provide high forecast accuracy.

Comparison Studies on Prediction Methods of Network Information System Survivability Situation

This paper presents a comparative study on GM model with residual modification and ARMA model. First set up a grey GM(1, 1) prediction model based on the original a series of data. In order to improve the accuracy of prediction, re-establish GM(1, 1) prediction model for residual tails and revise original GM(1, 1) prediction model with error modification. Then, use series of ARMA(p, q) models for modeling in original sequence. By comparing parameters of the candidate models, finally select MA (2) as appropriate for the prediction model. At Last, through the comparison of a variety of parameters, study their respective advantages and disadvantages of these two kinds of predictive models in the network information system survival situation.