Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorZhang, Ruien
dc.contributor.authorLan, Yuanen
dc.contributor.authorHuang, Guang-Binen
dc.contributor.authorXu, Zong-Benen
dc.identifier.citationZhang, R., Lan, Y., Huang, G.-B., & Xu, Z.-B. (2011). Universal Approximation of Extreme Learning Machine With Adaptive Growth of Hidden Nodes. IEEE Transactions on Neural Networks and Learning Systems, 23(2), 365-371.en
dc.description.abstractExtreme learning machines (ELMs) have been proposed for generalized single-hidden-layer feedforward networks which need not be neuron-like and perform well in both regression and classification applications. In this brief, we propose an ELM with adaptive growth of hidden nodes (AG-ELM), which provides a new approach for the automated design of networks. Different from other incremental ELMs (I-ELMs) whose existing hidden nodes are frozen when the new hidden nodes are added one by one, in AG-ELM the number of hidden nodes is determined in an adaptive way in the sense that the existing networks may be replaced by newly generated networks which have fewer hidden nodes and better generalization performance. We then prove that such an AG-ELM using Lebesgue p-integrable hidden activation functions can approximate any Lebesgue p-integrable function on a compact input set. Simulation results demonstrate and verify that this new approach can achieve a more compact network architecture than the I-ELM.en
dc.relation.ispartofseriesIEEE transactions on neural networks and learning systemsen
dc.rights© 2011 IEEEen
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen
dc.titleUniversal approximation of extreme learning machine with adaptive growth of hidden nodesen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
item.fulltextNo Fulltext-
Appears in Collections:EEE Journal Articles

Citations 5

Updated on Mar 14, 2023

Web of ScienceTM
Citations 5

Updated on Mar 18, 2023

Page view(s) 20

Updated on Mar 21, 2023

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.