A Novel Reformulated Radial Basis Function Neural Network

被引：0
|
作者：
Yin, Jianchuan [1 ]
Hu, Jiangqiang [1 ]
Bu, Renxiang [1 ]
机构：
[1] Dalian Maritime Univ, Coll Nav, Dalian 116026, Peoples R China
来源：
CCDC 2009: 21ST CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-6, PROCEEDINGS | 2009年
关键词：
Extreme Learning Machine (ELM); Radial Basis Function (RBF); Feedforward Networks;
D O I：
10.1109/CCDC.2009.5192355
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Single-hidden-layer feedforward networks (SLFNs) with radial basis function (RBF) hidden nodes are universal approximators when all the parameters of the networks are allowed adjustable. The learning speed of SLFNs is in general far slower than required and it has been a major bottleneck in their applications for past decades. Huang et al. propose a new learning algorithm called extreme learning machine (ELM) for SLFNs which randomly chooses hidden nodes and analytically determines the output weights. In this paper, common choices of RBF for generating ELM are analyzed and compared. The purpose of this study is to explore comparative strengths and weaknesses of the choices and to show some useful guidelines on how to choose an appropriate RBF hidden nodes for a particular problem.
引用
收藏
页码：2997 / 3001
页数：5
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