摘要
根据汽油辛烷值预测体系本身的非线性特点 ,提出主成分回归残差神经网络校正算法(principalcomponentregressionresidualartificialneuralnetwork ,PCRRANN)用于近红外测定汽油辛烷值的预测模型校正。该方法结合了主成分回归算法 (PC) ,与经典的线性校正算法PLS(PartialLeastSquare) ,PCR ,以及非线性PLS(NPLS ,Non linearPLS)等相比 ,预测能力有明显的改善。
A novel calibration algorithm, PCRRANN (principal component regression residual artificial neural network) method, was proposed based on the intrinsic non-linearity of the prediction of gasoline octane number, and then applied to the calibration of the prediction model of the near infra-red measurement of gasoline octane number. The method combined the linear calibration ability of the pricipal component regression (PCR) method and the excellent non-linear approximating ability of artificial neuralnetwork using the residual of PCR calibration as target signal and the PCR scores as input signal of the neuralnetwork respectively. Compared with the classical linear algorithms such as the PLS (partial least squares), PCR and NPLS (Non-linear PLS), the proposed method showed obvious improvement in prediction ability. The effects of the number of principal components of PCR part and some training parameters on the prediction model were also discussed.
出处
《分析化学》
SCIE
EI
CAS
CSCD
北大核心
2001年第1期87-91,共5页
Chinese Journal of Analytical Chemistry
关键词
主成分回归
神经网络
汽油
辛烷值
近红外光谱
测定
principal component regression
residual
neural network
gasoline
octane number
near infrared spectrocopy
