前向神经网络:一个新的非参数回归方法  

Feed-forward neural networks:A new approach to nonparametric regression

作　　者：李体政[1] 曹艳平[1] 李阳[1] 

机构地区：[1]西安建筑科技大学理学院,陕西西安710055

出　　处：《纺织高校基础科学学报》2009年第2期207-211,共5页Basic Sciences Journal of Textile Universities

摘　　要：从统计建模的观点,前向神经网络可以看作是一个新的非参数回归方法.通过模拟例子和实际例子对前向神经网络和局部多项式光滑方法的有限样本行为进行了对比,结果表明前向神经网络稍微优于局部多项式光滑方法.此外,对前向神经网络的优点和存在的问题进行了深入讨论.From the view of statistical modeling, the feed-forward neural networks can be regarded as a new ap proach to the nonparametric regression. The finite sample performance of the feed-forward neural networks is compared with that of the local polynomial smoothers by using simulated example and real example, the results demonstrate that the feed-forward neural networks performs a little better than the local polynomial smoothers. In addition, the advantages of the feed-forward neural networks and its difficulties in implementation are also dis cussed.

关 键 词：前向神经网络 非参数回归模型 局部多项式光滑 

分 类 号：O212.1[理学—概率论与数理统计]