矿渣微晶玻璃材料设计神经网络模型

Applications of Artificial Neural Network in Slag Glass-Ceramic Expert System

研究了人工神经网络在矿渣微晶玻璃材料设计中的应用。采用基于变尺度法的新学习算法建立了三层前馈型神经网络,发现当网络结构为M-2M-1,取一定范围内的学习误差时,网络具有很好的学习效果。研究证明,建立的人工神经网络模型学习速度快,收敛稳定,强壮性好,能根据较少的实验样本有效抽取矿渣微晶玻璃组成、工艺和性能之间的内在规律,是进行微晶玻璃材料设计的有力工具。

Artificial neural network Was introduced into slag glass-ceramic material designing. A 3 layers feedforward network. was built with a new robust learning. algorithm, based, on a concept of 'entire error modifying'. The network has a excellent learning ability when its topology is M-2M-1 and an appropriate.: study error chosen. The research results show that this slag glass-ceramic neural network is robust, quick and stable in training and data predicting, Which can disclose the relationship of elemental compositions, structure and material properties,of slag glass-ceramic effectively, even if some parameters are absent in samples.