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The Stability Classification Research of Roadway Surrounding Rock Based on Neural Network

Author: ZhangAiXia
Tutor: ShenYanMei
School: Henan Polytechnic University
Course: Applied Computer Technology
Keywords: Surrounding rock stability classification Classification index BP neural network Object-Oriented Analysis and Design
CLC: TP183
Type: Master's thesis
Year: 2009
Downloads: 71
Quote: 0
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Abstract


The emergence of artificial neural network theory for people to study the stability of surrounding rock classification provides a new theoretical approach . Since the stability of surrounding rock influence of many factors, and these factors roadway stability with complex nonlinear relationship between the presentation , the general approach is difficult to truly describe the nonlinear relationship , making the results difficult to spot classification the actual match . Artificial neural networks are composed of many neurons constitute a large-scale nonlinear dynamic systems , with strong nonlinear dynamic processing power. Therefore , this article uses artificial neural network approach to study the stability of surrounding rock classification . Based on the analysis of factors affecting the stability of surrounding rock start in a comprehensive analysis of the factors affecting the stability of surrounding rock , selected five pairs of relatively large impact on surrounding rock stability factor ; through the artificial neural network analysis of selected artificial neural networks the most mature , the most widely used BP neural network classification of surrounding rock stability ; BP neural network for slow convergence , easy to fall into local minima and other shortcomings in the analysis of the existing methods based on improved , proposed a law of momentum and variable learning rate method comprehensive improvement methods, and transfer function and the generalization ability of the network to make further improvements to enhance the network convergence speed and generalization ability , effectively avoid the local minimum point , and then create an improved BP neural network model , through the mine field data Xingdong training and testing, to get a complete neural network model ; Finally, object-oriented programming language developed roadway VC 6.0 stability of the classification system , made a simple -to-use visual interface .

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CLC: > Industrial Technology > Automation technology,computer technology > Automated basic theory > Artificial intelligence theory > Artificial Neural Networks and Computing
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