应用灰色关系集群和CGNN分析矿井深部巷道围岩的稳定性控制 毕业论文外文翻译精选.docVIP

英文原文 Application of Grey Relational Clustering and CGNN in Analyzing Stability Control of Surrounding Rocks in Deep Entry of Coal Mine Wanbin YANG 1, Zhiming QU2 （1.Beijing University of Science and Technology, Beijing, 100083； 2. Hebei University of Engineering, Handan, 056038） Abstract—With combination 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 CGNN 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 in combined grey neural network is stated and described in stably controlling the surrounding rocks in deep entry. I. INTRODUCTION GREY 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 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 inner 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 relations 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 man’s m