关键词: 核磁共振, 非线性模型, BP神经网络, 偶合常数

Abstract:

Linear models are often used to describe the relationship between nuclear spin-spin coupling constant and structural parameters, despite it has been shown that calculation using these models often result in large errors. Based on the results of previous works, a non-linear model was proposed in this study to describe the relationship between the spin-spin coupling constants of the C-F bonds and the chemical environment they are in. Back propagation (BP) neural network analysis was used to fit the experimental data to the model. The accuracy of the model proposed was tested in four compounds, and it was shown that the non-linear model fitted by BP neural network analysis provides much better predictions than the commonly used linear models. Calculation errors for the four test compounds were less than 0.4% when the nonlinear model was used.

Key words: NMR, non-linear regression, BP neural network, spin-spin coupling constant