摘要
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.
出处
《化学学报》
SCIE
CAS
CSCD
北大核心
2000年第11期1409-1412,共4页
Acta Chimica Sinica
基金
国家自然科学基金(29877016)
