摘要
矿岩可崩性是地下矿山能否用自然崩落法开采的重要依据之一 ,利用人工神经网络 (ANN)进行矿岩可崩性分级识别 ,有利于真实地描述矿岩可崩性与其影响因素之间的非线性关系。为此 ,采用人工神经网络理论 ,建立了矿岩可崩性的神经网络识别模型 ,结合工程实例对其矿岩可崩性进行分级识别 。
The cavability of ore rock is one of the major factors that decide the possibility for an underground mine to use natural block caving method. The application of ANN to recognize the cavability classification of ore rock can facilitate the factual description of the nonlinear relation between the cavability and its influence factors. Therefore, the ANN was used to establish a recognition model of the ore rock cavability and the model's application in real engineering cases has proved its feasibility and effectiveness.
出处
《金属矿山》
CAS
北大核心
2003年第2期32-33,42,共3页
Metal Mine
关键词
地下矿山
矿岩可崩性
自然崩落法
人工神经网络
识别方法
Natural black caving method, Cavability of ore rock, Classification,Artificial neural network
