一种改进的材料成分神经网络模型

An Improved Model for the Components of Materials Based on Neural Network Method

关键词:  神经网络模型  训练过程  训练方式  特征  样本  有效性  根据  RBF  仿真结果  径向基函数

Key words:  neural network,radial basis function(RBF),composition of material,feature selection

摘要:

所介绍的材料成分神经网络模型使用的是一种改进的4层径向基函数(RBF)神经网络，其基本思想是根据样本的不同特征采用不同的训练方式，并且在训练过程中根据样本的特征添加隐层节点来加快网络的训练过程．网络的映射选用区域映射方式，可有效防止网络的过拟和，同时也可提高网络的识别效果．对建筑材料系统中相图的仿真结果表明了该方法的有效性。

Abstract:

In this paper a model for the components of materials based on neural network is introduced. This model is an improved four-layer feed-forward radial basis function (RBF) neural network that uses different training ways according to different characteristics of the patterns, and adds the centers in hide-lay for increasing training speed. The regional mapping from the input's parameters to the output's prevents the network from "excessive learning" and improves the ability of recognition. The simulation of CaO-Al_2O_3-SiO_2 system shows the validity of this method.