采用BP神经网络研究C-F键核自旋偶合常数  

Calculation of Nuclear Spin-Spin Coupling Constants of C-F Bonds by A Nonlinear Model and Back Propagation (BP) Neural Network Analysis

作　　者：吴雪梅[1] 杨晓慧[1] 刘志强[2] 韩敏[1] 范磊刚 廖显威[3] 

机构地区：[1]西安文理学院化学系,陕西西安710065 [2]第二炮兵工程学院,陕西西安710025 [3]四川师范大学化学学院,四川成都610068

出　　处：《波谱学杂志》2005年第3期269-276,共8页Chinese Journal of Magnetic Resonance

基　　金：西安文理学院专项科研基金资助项目.

摘　　要：通常理论研究核自旋偶合常数的方法是基于线性模型进行拟合和预测,该方法在拟合和预测中仍有较大误差.本文在前面工作的基础上,提出了基于非线性模型对C-F键核自旋偶合常数进行研究的观点,采用BP神经网络方法对C-F键核自旋偶合常数的函数关系式进行拟合,并用拟合结果对4种化合物的偶合常数进行预测.结果表明,采用非线性的BP神经网络方法其训练效果与预测效果均优于线性模型方法;其预测误差对文中的4种化合物不超过0.40%.Linear models are often used to describe the relationship between nuclear spinspin 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%o when the nonlinear model was used.

关 键 词：核磁共振 非线性模型 BP神经网络 偶合常数 

分 类 号：O641.13[理学—物理化学]