摘要
胶结强度的主要影响因素有:级配、胶结剂含量、胶结料浆浓度等。神经网络具有大规模并行处理,分布式信息储存以及很强的学习能力,是解决数据间非线性映射关系的有力工具。因此,在一定实验基础下,利用神经网络建立级配、胶结剂含量、胶结料浆浓度与胶结强度4者的神经网络模型,可以有效地模拟预测胶结强度,为矿山充填胶结强度的计算提供指导作用。
The main factors affecting cementing strength include gradation, cement content, concentration of cement slurry. The neural network which have the storage ability of massively parallel processing and distributed information storage is a powerful tool to deal with the nonlinear relationship. As a result, based on certain experimental basis, use neural network can build a prediction model of cementing strength which can effectively simulate or predicted cementing strength. It also can proviSe guidance for calculation of mine filling.
出处
《价值工程》
2016年第3期92-94,共3页
Value Engineering
基金
国家自然科学基金项目(51164016)
关键词
神经网络
胶结强度
预测模型
neural network
cementing strength
prediction model
