An Enhanced Online Sequential Extreme Learning Machine algorithm

Author

Jun, Yu ; Er, Meng Joo

Author_Institution

Nanyang Technol. Univ., Nanyang

fYear

2008

fDate

2-4 July 2008

Firstpage

2902

Lastpage

2907

Abstract

In this paper, an enhanced online sequential extreme learning machine (EOS-ELM) algorithm for single-hidden layer feedforward neural networks (SLFNs) with radial basis function (RBF) hidden nodes is proposed. The proposed EOS-ELM algorithm is an enhanced version of the OS-ELM of [8], which has been shown to be extremely fast with generalization performance better than other sequential training methods. The EOS-ELM algorithm adapts the node location, adjustment and pruning method of the MRAN of [3], so that the number of hidden nodes used in the OS-ELM can be modified. Simulation results show that the generalization performance of EOS-ELM is comparable to the OS-ELM and the number of nodes used by the EOS-ELM is reduced significantly.

Keywords

generalisation (artificial intelligence); learning (artificial intelligence); radial basis function networks; generalization performance; online sequential extreme learning machine algorithm; radial basis function hidden nodes; sequential training method; single-hidden layer feedforward neural network; Machine learning; SLFN; adaptation of the node location; extreme learning machine; neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference, 2008. CCDC 2008. Chinese

Conference_Location

Yantai, Shandong

Print_ISBN

978-1-4244-1733-9

Electronic_ISBN

978-1-4244-1734-6

Type

conf

DOI

10.1109/CCDC.2008.4597855

Filename

4597855