文章基本信息

标题：An Algorithm to Determine RBFNN’s Center Based on the Improved Density Method
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作者：Mingwen Zheng ; Yanping Zhang
期刊名称：Open Journal of Applied Sciences
印刷版ISSN：2165-3917
电子版ISSN：2165-3925
出版年度：2014
卷号：4
期号：1
页码：1-5
DOI：10.4236/ojapps.2014.41001
出版社：Scientific Research Publishing
摘要：It takes more time and is easier to fall into the local minimum value when using the traditional full-supervised learning algorithm to train RBFNN. Therefore, the paper proposes one algorithm to determine the RBFNN’s data center based on the improvement density method. First it uses the improved density method to select RBFNN’s data center, and calculates the expansion constant of each center, then only trains the network weight with the gradient descent method. To compare this method with full-supervised gradient descent method, the time not only has obvious reduction (including to choose data center’s time by density method), but also obtains better classification results when using the data set in UCI to carry on the test to the network.
关键词：Radial Basis Function Neural Network; Data Center; Expansion Constant; Density Method; Full-Supervised Algorithm