摘要
以层次划分和模块化为思想基础,提出了一种新型神经网络模型对自由曲面进行重构,即基于径向基函数(RBF)神经网络的混合网络模型。先后运用减聚类方法、正交最小二乘法、最大似然法对网络进行有无监督的混合训练,旨在解决大样本集的简化建模和快速训练问题,提高混合网络输出精度。实验结果表明该网络模型使得曲面的拟合精度有了明显提高。
This article proposed one kind of new nerve network model,on hasis of the thought of the level division and the module,namely RBF mixture neural network model.Successively utilizes the way of reduce-gathers,OLS and the maximum likelihood method in order to solve the problem of modeling and the fast training for big sample collection simplification and enhance the output precision of mixture neural network.The test result indicated the filling precision of fi＇eeform surface was enhanced distinctly by this network model.
出处
《计算机工程与应用》
CSCD
北大核心
2007年第32期80-82,共3页
Computer Engineering and Applications
基金
国家自然科学基金(No.60173055)。~~
关键词
RBF神经网络
混合神经网络
曲面重构
RBF neural networks
mixture neural network
freeform surface reconstruction
