A Novel Reformulated Radial Basis Function Neural Network
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.
Eztreme Learning Machine (ELM) Radial Basis Function (RBF) Feedforward Networks
Jianchuan Yin Jiangqiang Hu Renxiang Bu
College of Navigation, Dalian Maritime University, Dalian 116026
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
2997-3001
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）