一种改进BP网络结构用于化学中的非线性校正

Using Modified BP Neural Network for Non-Linear Modeling in Chemistry

针对化学领域中的非线性关系特点,在常规BP网络基础上,提出了一种“杂交”型BP网络,包含两个隐层,并有输入层到输出层的直连接.它可很好地解释数据中同时存在的线性及非线性关系,效果优于多元回归法及普通BP算法.

BP neural network' s excellence in non - linear modeling is shown with two examples. The first is the non - linear calibration for the relationship between the infrared reflectance rates and contents of protein. The second is QSAR, predicting the physical property of some compounds by using the structural parameters. By employing a new mode of BP neural network, with two hidden layers and direct connections from input to output layer, better results than those of multivariate linear regression and normal BP neural network are achieved.