原地爆破浸出筑堆块度预测的神经网络方法研究

针对现有爆破块度预测模型存在的不足，应用神经网络理论，对原地爆破浸出爆破筑堆块度的预测进行了研究。根据原地爆破浸出工艺爆破筑堆的技术特点，构建了3层前馈型神经网络结构。基于国内外原地爆破浸出爆破筑堆工程实例，采用BP算法对网络进行了训练，建立了原地爆破浸出工艺爆破筑堆块度分布与其影响因素间的非线性映射关系。采用测试样本对模型进行了测试，结果表明：所建立的模型用于原地爆破浸出爆破筑堆块度的预测是可行的，模型精度是可靠的。

An Artificial Neural Network Approach to Predicting the Fragment Sizeof Ore Stacking of In-situ Blasting and Leaching

In view of the shortcomings of the current prediction models of blasting fragmentation, artificial neural network theory was utilized to research on fragmentation prediction of ore stacking of in-situ blasting and leaching . According to the tectonic features of ore stacking by blasting for in-situ leaching, the three-layer feedforward neural network was established. Based on the practical engineers of home and abroad in-situ blasting and leaching, the artificial neural network model was trained by use of back propagation algorithm, so the nonlinear map relationship between fragmentation distribution of ore stacking by blasting and its influential factors was established. Furthermore, based the existing examples, the predicted results of the neural network were tested with ones of in-situ measurements, and the result has shown that the neural network model is feasible and exact for predicting the fragment size of ore stacking of in-situ blasting and leaching.