A hybrid online sequential extreme learning machine with simplified hidden network
Er, M. J.
Zhai, L. Y.
Date of Issue2012
School of Electrical and Electronic Engineering
Singapore Institute of Manufacturing Technology
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.
DRNTU::Engineering::Computer science and engineering
IAENG International journal of computer science
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