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1、英文原文Application of Grey Relational Clustering and CGNN in Analyzing Stability Control of Surrounding Rocks in Deep Entry of Coal MineWanbin YANG 1, Zhiming QU2（1.Beijing University of Science and Technology, Beijing, 100083；2. Hebei University of Engineering, Handan, 056038）AbstractWith combination
2、of grey neural network （CGNN） and grey relational clustering, the models are constructed, which are used to solve the prediction and coMParison of surrounding rocks stability controlling parameters in deep entry of coal mine.The results show that grey relational clustering is an effective way and CG
3、NN has perfect ability to be studied in a short-term prediction. Combined grey neural network has the features of trend and fluctuation while combining with the time-dependent sequence prediction. It is concluded that great improvements coMPared with any methods of trend prediction and simple factor
4、 in combined grey neural network is stated and described in stably controlling the surrounding rocks in deep entry.I. INTRODUCTIONGREY system technology states the uncertainty of small sample and poor information. With the development and generation of the unknown information, the real world will be
5、 discovered and the system operation behavior will be mastered properly. Through original stability with the pre-processing, the grey system law will be described. Though the real world is expressed complicatedly and the satisfied irregularly, the integrated functions will be appeared as a certain i
6、nner regular pattern 1. The studying of grey system technology is based on the poor information which is generated by parts of the known information to extract valuable stability and to properly recognize and effectively control the system behavior. The neural network is dependent on its inner relat
7、ions to model, which is well self-organized and self-adapted. The neural network can conquer the difficulties of traditionally quantitative prediction and avoid the disturbance of mans mind. The grey relational analysis is based on the similarity of geometric parameters curve to determine the relati
8、on degree.The closer the curve shape similarity is, the greater the corresponding sequence correlation is. The similarity is described with correlation coefficient and correlation degree which describes the effect on the results by various factors.The greater the correlation is, the greater the iMPa
9、ct extent is.While analyzing a practical system, the data series with the behavioral characteristics are identified. Additionally, it is necessary to ascertain the effective factors influencing system behavior characteristics, namely, sub-factors 1, 2.Though the objective system are expressed compli
10、catedly,the development and change are still of logic laws and the different functions are coordinated and unified. Therefore,how to find its inner developing regularities from the dispersed stability seems to be important. In the light of the description above, it can be found that the combination
11、among grey relational clustering will take great effect on stability control of surrounding rocks in deep entry in coal mine. The combined grey neural network model will be built in solving and analyzing this problem.II. GREY RELATIONAL CLUSTERINGA. Grey Relational ClusteringAs the general system of
12、 grey trend relation, D. J. CHEN, etal 3-6 has done a lot of work. In order to apply it into the practice, the basic idea about his study is introduced. The similarity and approximation in the dynamic system behavior can be expressed using grey trend relation （GTR） With the aid of GTR, the implicit
13、system operation laws maybe stated aptly.Generally, the general system theory is applied in the general GTR system, and combining with GTR, the systemized models of GTR analysis will be deduced.It is assumed that U is the referred factor set and W the coMPared factor set, uy R is the set of GTR in （
14、B, W） The matrix is called the GTR matrix for ， and , while the set （B, W） is finite. Where , the trend relation and ,the trend relation function. ., And Q is the general GTR system, . In order to illustrate and serve application in this paper,some definitions are introduced here. V is the evaluatin
15、g space of system Q and H is the evaluating functions. Thus, the relation between Q and evaluating space are described as .Therefore, the general GTR system model is defined as ;.The general GTR system is generalized, which includes the problems using the GTR analysis. In order to solve different pr
16、oblems, the GTR should be not alike in the light of B, W and H. On the basis of GTR matrix, the GTR clustering method is to assemble the observed index or objects into many definable classifications. The clustering can be seen as the observed object set of the same classification. Actually, any observed objects have many characteristic indexes which are not accurately classified. Through GTR clustering, the factors of the same classific
