A hybrid online sequential extreme learning machine with simplified hidden network

• Main Authors: Li, X., Er, M. J., San, L., Zhai, L. Y.
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2014
• Subjects: DRNTU::Engineering::Computer science and engineering
• Online Access: https://hdl.handle.net/10356/106191; http://hdl.handle.net/10220/23965
• Institution: Nanyang Technological University

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.