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基于地下岩体赋存环境复杂性的特点,采面顶板类型识别问题是一个比较复杂的问题,它受制于岩石强度R_c、裂隙间距I、分层厚度h、垮落步距L等众多的影响因素,这是一典型的复杂非线性系统问题,因而用传统数学方法所建模型误差较大。本文运用人工神经元网络建立了采面顶板类型识别的神经网络模型,进行了实例研究,并同用模糊数学方法所建模型进行了对比。图1,表1,参8。
Based on the characteristics of the complexity of the environment in which underground rock mass is stored, the identification of roof type in mining face is a complex problem. It is subject to many influences such as rock strength Rc, crack spacing I, stratification thickness h, caving pace L Factor, which is a typical complex nonlinear system problems, so the use of traditional mathematical methods to build a larger model error. In this paper, artificial neural network is used to establish a neural network model of roof identification of mining face, and a case study is made. The model is compared with that of the fuzzy mathematics model. Figure 1, Table 1, reference 8.