采用BP神经网络研究C—F键核自旋偶合常数
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%....
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| Published in | Bopuxue zazhi Vol. 22; no. 3; pp. 269 - 276 |
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| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
西安文理学院,化学系,陕西,西安,710065%第二炮兵工程学院,陕西,西安,710025%四川师范大学,化学学院,四川,成都,610068
2005
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1000-4556 |
| DOI | 10.3969/j.issn.1000-4556.2005.03.005 |
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| Abstract | 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%. |
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| AbstractList | 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%. O641.13; 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%. |
| Author | 吴雪梅 杨晓慧 刘志强 韩敏 范磊刚 廖显威 |
| AuthorAffiliation | 西安文理学院化学系,陕西西安710066 第二炮兵工程学院,陕西西安710025 四川师范大学化学学院,四川成都610068 |
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| Author_FL | HAN Min FAN Lei-gang YANG Xiao-hui LIAO Xian-wei WU Xue-mei LIU Zhi-qiang |
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| DocumentTitleAlternate | Calculation of Nuclear Spin-Spin Coupling Constants of C--F Bonds by A Nonlinear Model and Back Propagation (BP) Neural Network Analysis |
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| Keywords | 偶合常数 核磁共振 BP神经网络 非线性模型 |
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| SubjectTerms | BP神经网络 偶合常数 核磁共振 非线性模型 |
| Title | 采用BP神经网络研究C—F键核自旋偶合常数 |
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