Forecasting Model of Coal Requirement Quantity Based on Grey System Theory

The generally used methods of forecasting coal requirement quantity include the analogy method, the outside push method and the cause effect analysis method. However, the precision of forecasting results using these methods is lower. This paper uses the grey system theory, and sets up grey forecasting model GM (1, 3) to coal requirement quantity. The forecasting result for the Chinese coal requirement quantity coincides with the actual values, and this shows that the model is reliable. Finally, this model are used to forecast Chinese coal requirement quantity in the future ten years.

Application of Grey Theory to Ionospheric Short-term Forecasting

By analysis of historical data of the ionosphere, it is suggested to apply grey theory to ionospheric short-term forecasting, grey range information entropy is defined to determine the optimum grey length of the sample sequence, the prediction model based on residual error is constructed, and the observation data of multiple ionospheric observation stations in China are adopted for test. The prediction result indicates that the average grey range information entropy calculation results reflect the cyclical effects of solar rotation, precision of the forecasting method in high latitudes is higher than low latitudes, and its error is large relatively in more intense solar activity season, the effect of forecasting 1 day in advance of average relative residuals are less than 1 MHz, the average precision is more than 90%. It provides a new way of thinking for the ionospheric foF2 short-term forecast in the future.

Interval grey number sequence prediction by using non-homogenous exponential discrete grey forecasting model

This paper aims to study a new grey prediction approach and its solution for forecasting the main system variable whose accurate value could not be collected while the potential value set could be defined. Based on the traditional nonhomogenous discrete grey forecasting model（NDGM）, the interval grey number and its algebra operations are redefined and combined with the NDGM model to construct a new interval grey number sequence prediction approach. The solving principle of the model is analyzed, the new accuracy evaluation indices, i.e. mean absolute percentage error of mean value sequence（MAPEM） and mean percent of interval sequence simulating value set covered（MPSVSC）, are defined and, the procedure of the interval grey number sequence based the NDGM（IG-NDGM） is given out. Finally, a numerical case is used to test the modelling accuracy of the proposed model. Results show that the proposed approach could solve the interval grey number sequence prediction problem and it is much better than the traditional DGM（1,1） model and GM（1,1） model.

TRIZ TECHNOLOGY FORECASTING AS QFD INPUT WITHIN THE NPD ACTIVITIES

As a result of the fierceness of business competition, companies, to remaincompetitive, have to charm their customers by anticipating their needs and being able to rapidlydevelop exciting new products for them. To overcome this challenge, technology forecasting isconsidered as a powerful tool in today's business environment, while there are as many successstories as there are failures, a good application of this method will give a good result. Amethodology of integration of patterns or lines of technology evolution in TRIZ parlance ispresented, which is also known as TRIZ technology forecasting, as input to the QFD process to designa new product. For this purpose, TRIZ technology forecasting, one of the TRIZ major tools, isdiscussed and some benefits compared to the traditional forecasting techniques are highlighted. Thena methodology to integrate TRIZ technology forecasting and QFD process is highlighted.

Forecast of Flood in Chaohu Lake Basin of China Based on Grey-Markov Theory

Flood is one kind of unexpected and the most common natural disasters, which is affected by many factors and has complex mechanism. At home and abroad, there is still no mature theory and method used for the long-term forecast of natural precipitation at present. In the present paper the disadvantages of grey GM (1, 1) and Markov chain are ana- lyzed, and Grey-Markov forecast theory about flood is put forward and then the modifying model is developed by making prediction of Chaohu Lake basin. Hydrological law was conducted based on the theoretical forecasts by grey system GM (1, 1) forecast model with improved Markov chain. The above method contained Stat-analysis, embodying scientific approach, precise forecast and its reliable results.

STUDY ON GREY FORECASTING MODEL OF COPPER EXTRACTION RATE WITH BIOLEACHING OF PRIMARY SULFIDE ORE

A model GM （grey model） （1,1） for forecasting the rate of copper extraction during the bioleaching of primary sulphide ore was established on the basis of the mathematical theory and the modeling process of grey system theory. It was used for forecasting the rate of copper extraction from the primary sulfide ore during a laboratory microbial column leaching experiment. The precision of the forecasted results were examined and modified via ＂posterior variance examination＂. The results show that the forecasted values coincide with the experimental values. GM （1,1） model has high forecast accuracy; and it is suitable for simulation control and prediction analysis of the original data series of the processes that have grey characteristics, such as mining, metallurgical and mineral processing, etc. The leaching rate of such copper sulphide ore is low. The grey forecasting result indicates that the rate of copper extraction is approximately 20% even after leaching for six months.

Forecasting model of residential load based on general regression neural network and PSO-Bayes least squares support vector machine

Firstly,general regression neural network(GRNN) was used for variable selection of key influencing factors of residential load(RL) forecasting.Secondly,the key influencing factors chosen by GRNN were used as the input and output terminals of urban and rural RL for simulating and learning.In addition,the suitable parameters of final model were obtained through applying the evidence theory to combine the optimization results which were calculated with the PSO method and the Bayes theory.Then,the model of PSO-Bayes least squares support vector machine(PSO-Bayes-LS-SVM) was established.A case study was then provided for the learning and testing.The empirical analysis results show that the mean square errors of urban and rural RL forecast are 0.02% and 0.04%,respectively.At last,taking a specific province RL in China as an example,the forecast results of RL from 2011 to 2015 were obtained.

Alternative techniques for forecasting mineral commodity prices

Forecasting mineral commodity(MC) prices has been an important and difficult task traditionally addressed by econometric, stochastic-Gaussian and time series techniques. None of these techniques has proved suitable to represent the dynamic behavior and time related nature of MC markets. Chaos theory(CT) and machine learning(ML) techniques are able to represent the temporal relationships of variables and their evolution has been used separately to better understand and represent MC markets. CT can determine a system's dynamics in the form of time delay and embedding dimension. However, this information has often been solely used to describe the system's behavior and not for forecasting.Compared to traditional techniques, ML has better performance for forecasting MC prices, due to its capacity for finding patterns governing the system's dynamics. However, the rational nature of economic problems increases concerns regarding the use of hidden patterns for forecasting. Therefore, it is uncertain if variables selected and hidden patterns found by ML can represent the economic rationality.Despite their refined features for representing system dynamics, the separate use of either CT or ML does not provide the expected realistic accuracy. By itself, neither CT nor ML are able to identify the main variables affecting systems, recognize the relation and influence of variables though time, and discover hidden patterns governing systems evolution simultaneously. This paper discusses the necessity to adapt and combine CT and ML to obtain a more realistic representation of MC market behavior to forecast long-term price trends.

Application of the third theory of quantification in coal and gas outburst forecast

The essential principles of the third theory of quantification are discussed, the concept and calculated method of reaction degree are put forward which extend the ap- plying range and scientificalness of the primary reaction. Taking the Zhongmacun Mine as example, on the base of analyzing the rules of gas geology synthetically and travers- ing the geological factors infecting coal and gas outburst, the paper adopts the method of combining carving up statistical units with the third theory of quantification, screens out 8 sensitive geological factors from 11 geological indexes and carries through the work of gas geology regionalism to the exploited area of Zhongmacun according to the researching result. The practice shows that it is feasible to apply the third theory of quantification to gas geology, which offers a new thought to screen the sensitive geo- logical factors of gas outburst forecast.

Using Grey System Theory for Earthquake Forecast

By combining conventional grey correlation analysis, grey clustering method and grey forecasting methods with our multi-goal forecast thoughts and the techniques of grey time series processing, we develop six different grey earthquake forecast models in this paper. Using the record of major earthquakes in Japan from 1872 to 1995, we forecast future earthquakes in Japan. We develop an earthquake forecast model. By using the major earthquakes in Japan from 1872 to 1984, we forecast earthquakes from 1985 to 1995 and check the precision of the grey earthquake models. We find that the grey system theory can be applied to earthquake forecast. We introduce the above analysis methods and give a real example to evaluate and forecast. We also further discuss the problems of how to improve the precision of earthquake forecast and how to strengthen the forecast models in future research.

TIME SERIES NEURAL NETWORK FORECASTING METHODS

This paper has discussed the possibility and key problem to construct the neural network time series model, and three time series neural network forecasting methods has been proposed, i. e. a neural network nonlinear time series model, a neural network multi-dimension time series model and a neural network combining predictive model. These three methods are applied to real problems. The results show that these methods are better than the traditional one. Furthermore, the neural network methods are compared with the traditional method, and the constructed model of intellectual information forecasting system is given.

To a forecasting of the wear-resistance of friction units details parameters question

The process of wear process expedient considering as set of special cases wear. For each special case wear were received settlement with reference to some certain conditions of experimental researches realisation, uniting in self SLOP. For successful application deduced dependence at forecasting wear-resistance the mechanism of reduction is developed which allows to distribute deduced dependencies to any conditions of wear process.

Improved grey-based approach for power demand forecasting

Grey theory is a multidisciplinary and generic theory to cope with systems of poor or deficient information. We proposed in this paper an improved grey method (GM) to overcome the disadvantages of the general GM(1,1). In the improved GM(1,1), a new background value formula is deduced and Markov-chain sign estimation is imbedded into the residual modification model. We tested the efficiency and accuracy of our model by applying it to the power demand forecasting in Taiwan. Experimental results demonstrate the new method has obviously a higher prediction accuracy than the general model.

High-precision chaotic radial basis function neural network model:Data forecasting for the Earth electromagnetic signal before a strong earthquake

The Earth’s natural pulse electromagnetic field data consists typically of an underlying variation tendency of intensity and irregularities.The change tendency may be related to the occurrence of earthquake disasters.Forecasting of the underlying intensity trend plays an important role in the analysis of data and disaster monitoring.Combining chaos theory and the radial basis function neural network,this paper proposes a forecasting model of the chaotic radial basis function neural network to conduct underlying intensity trend forecasting by the Earth’s natural pulse electromagnetic field signal.The main strategy of this forecasting model is to obtain parameters as the basis for optimizing the radial basis function neural network and to forecast the reconstructed Earth’s natural pulse electromagnetic field data.In verification experiments,we employ the 3 and 6 days’data of two channels as training samples to forecast the 14 and 21-day Earth’s natural pulse electromagnetic field data respectively.According to the forecasting results and absolute error results,the chaotic radial basis function forecasting model can fit the fluctuation trend of the actual signal strength,effectively reduce the forecasting error compared with the traditional radial basis function model.Hence,this network may be useful for studying the characteristics of the Earth’s natural pulse electromagnetic field signal before a strong earthquake and we hope it can contribute to the electromagnetic anomaly monitoring before the earthquake.

Chaotic Load Series Forecasting Based on MPMR

Minimax probability machine regression （MPMR） was proposed for chaotic load time series global prediction. In MPMR, regression function maximizes the minimum probability that future predication will be within an ε to the true regression function. After exploring the principle of MPMR, and verifying the chaotic property of the load series from a certain power system, one-day-ahead predictions for 24 time points next day wcre done with MPMR. Thc results demonstrate that MPMP has satisfactory prediction efficiency. Kernel function shape parameter and regression tube value may influence the MPMR-based system performance. In the experiments, cross validation was used to choose the two parameters.

A New Method for Forecasting the Life Test Data of Mechanical Products

The model for forecasting the test data on mechanical products is established in the application of the grey system theories. A new formula of the background value is introduced into the model. The result of an example shows the method can reduce test expense and enhance the precision of forecasting.

Analysis on the Theory of Blow-up and Verification on Numerical Prediction of Heavy Rain in Sichuan Basin

By dint of V-3θ diagram from the Blown-up theory,a continuous heavy rain process in western Sichuan basin from July 14 to 17,2009 is analyzed in this paper.Situation field and precipitation of ECWMF and T213 are verified and discussed.Results show that V-3θ diagram can describe the heavy rain process accurately.Combining with additional conventional weather charts,experience and numerical forecast products,the heavy rain falling area is determined.The forecast accuracy of situation field of EC is significantly higher than that of T213 and the forecast accuracy of T213 for heavy rain forecast is relatively low.

Application of the Third Theory of Quantification in Screening Sensitive Geological Factors Influencing Coal and Gas Outburst

The principles of the third theory of quantification were discussed. The concept and calculation method of reaction degree were put forward, which have extended the applying range and scientificalness of the primary reaction. Taking the Zhongmacun mine as an example, the geological factors affecting coal and gas outburst were researched. Eight sensitive factors for the outburst of coal and gas were screened out from 11 geological factors using the method of unit classification and the third theory of quantification. On the basis of this, the Zhongmacun coal mine was classified into several divisions. The practice shows that it is feasible to apply the third theory of quantification to gas geology, which offers a new thought to screen the sensitive geological factors of gas outburst forecast.

A New Way to Predict Forecast Skill

Forecast skill (Anomaly Correlated Coefficient, ACC) is a quantity to show the forecast quality of the products of numerical weather forecasting models. Predicting forecast skill, which is the foundation of ensemble forecasting, means submitting products to predict their forecast quality before they are used. Checking the reason is to understand the predictability for the real cases. This kind of forecasting service has been put into operational use by statistical methods previously at the National Meteorological Center (NMC), USA (now called the National Center for Environmental Prediction (NCEP)) and European Center for Medium-range Weather Forecast (ECMWF). However, this kind of service is far from satisfactory because only a single variable is used with the statistical method. In this paper, a new way based on the Grey Control Theory with multiple predictors to predict forecast skill of forecast products of the T42L9 of the NMC, China Meteorological Administration (CMA) is introduced. The results show: (1) The correlation coefficients between 'forecasted' and real forecast skill range from 0.56 to 0.7 at different seasons during the two-year period. (2) The grey forecasting model GM(1,8) forecasts successfully the high peaks, the increasing or decreasing tendency, and the turning points of the change of forecast skill of cases from 5 January 1990 to 29 February 1992.