A new Training Algorithm for RBF Neural Network based on Dynamic Fuzzy Clustering

被引：0
|
作者：
Cui, Yan-Jun [1 ,2 ,3 ]
Ma, Yan-Dong [1 ,2 ]
Li, Jie [4 ]
Zhao, Zheng [3 ]
机构：
[1] Hebei Acad Sci, Inst Appl Math, Shijiazhuang 050081, Peoples R China
[2] Hebei Authenticat Technol Engn Res Ctr, Shijiazhuang 050081, Peoples R China
[3] Tianjin Univ, Tianjin 300072, Peoples R China
[4] Hebei Univ, Fac Math & Comp Sci, Machine Learning Ctr, Baoding 071002, Peoples R China
来源：
INDUSTRIAL INSTRUMENTATION AND CONTROL SYSTEMS, PTS 1-4 | 2013年 / 241-244卷
关键词：
Radial basis function neural network (RBFNN); Two-phase method; Dynamic fuzzy cluster method (DFCM);
D O I：
10.4028/www.scientific.net/AMM.241-244.1593
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A new algorithm for training radial basis function neural network (RBFNN) is presented in this paper. This algorithm is based on the dynamic fuzzy clustering method (DFCM). The algorithm has a number of advantages compared to the traditional method based on k-means. For example, it does not need to know the number of the hidden nodes and to predicts more accurately. Due to these advantages, this method proves to be suitable for developing models for complex nonlinear systems.
引用
收藏
页码：1593 / +
页数：2
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