一种基于神经网络的材料成分模型

为研究如何利用神经网络预测材料化合物构成，建立了一个4层前向型网络，这种网络通过改变神经元非线性变换函数的参数，使连接权调整线性化，从而可提高学习速度，减少计算量，并避免了BP网络存在的易陷入局部极小和收敛速度慢的问题，以CaO-Al2O3-SiO2系统为例进行的仿真研究结果表明，该网络可成功包含材料化合物的构成信息。

A Model for the Components of Materials Based on Neural Network Method

This paper is concerned with using the neural network to predict the model for the materials composition. A four layer feed forward radial basis function neural network is built, so that a nonlinear mapping through changing the parameter of the nonlinear activity function is realized, which reflects on the linearization when adjusting the connecting weight. Therefore, it can accelerate the learning speed and reduce the quantity of calculation, also the problem of local minimum and the slow rate of convergence of the back propagation neural netwok are avoided. In this paper CaO Al 2O 3 SiO 2 system is discussed as an example. Simulation shows that neural network can be used to predict the materials composition successfully.