Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/106191
Title: A hybrid online sequential extreme learning machine with simplified hidden network
Authors: Li, X.
Er, M. J.
San, L.
Zhai, L. Y.
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2012
Source: Er, M. J., Zhai, L. Y., Li, X., & San, L. (2012). A hybrid online sequential extreme learning machine with simplified hidden network. IAENG International journal of computer science, 39(1), 1-9.
Series/Report no.: IAENG International journal of computer science
Abstract: In this paper, a novel learning algorithm termed Hybrid Online Sequential Extreme Learning Machine (HOS-ELM) is proposed. The proposed HOS-ELM algorithm is a fusion of the Online Sequential Extreme Learning Machine (OS-ELM) and the Minimal Resource Allocation Network (MRAN). It is capable of reducing the number of hidden nodes in Single-hidden Layer Feed-forward Neural Networks (SLFNs) with Radial Basis Function (RBF) by virtue of adjustment in node allocation and pruning capability. Simulation results show that the generalization performance of the proposed HOS-ELM is comparable to the original OS- ELM with significant reduction in the number of hidden nodes.
URI: https://hdl.handle.net/10356/106191
http://hdl.handle.net/10220/23965
ISSN: 1819-656X
Rights: © 2012 IAENG International Journal of Computer Science. This paper was published in IAENG International Journal of Computer Science and is made available as an electronic reprint (preprint) with permission of IAENG International Journal of Computer Science. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles
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