Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.neucom.2007.07.025
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dc.titleIncremental extreme learning machine with fully complex hidden nodes
dc.contributor.authorHuang, G.-B.
dc.contributor.authorLi, M.-B.
dc.contributor.authorChen, L.
dc.contributor.authorSiew, C.-K.
dc.date.accessioned2013-07-04T07:50:35Z
dc.date.available2013-07-04T07:50:35Z
dc.date.issued2008
dc.identifier.citationHuang, G.-B., Li, M.-B., Chen, L., Siew, C.-K. (2008). Incremental extreme learning machine with fully complex hidden nodes. Neurocomputing 71 (4-6) : 576-583. ScholarBank@NUS Repository. https://doi.org/10.1016/j.neucom.2007.07.025
dc.identifier.issn09252312
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/39831
dc.description.abstractHuang et al. [Universal approximation using incremental constructive feedforward networks with random hidden nodes, IEEE Trans. Neural Networks 17(4) (2006) 879-892] has recently proposed an incremental extreme learning machine (I-ELM), which randomly adds hidden nodes incrementally and analytically determines the output weights. Although hidden nodes are generated randomly, the network constructed by I-ELM remains as a universal approximator. This paper extends I-ELM from the real domain to the complex domain. We show that, as long as the hidden layer activation function is complex continuous discriminatory or complex bounded nonlinear piecewise continuous, I-ELM can still approximate any target functions in the complex domain. The universal capability of the I-ELM in the complex domain is further verified by two function approximations and one channel equalization problems. © 2007 Elsevier B.V. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.neucom.2007.07.025
dc.sourceScopus
dc.subjectChannel equalization
dc.subjectComplex activation function
dc.subjectConstructive networks
dc.subjectELM
dc.subjectFeedforward networks
dc.subjectI-ELM
dc.typeArticle
dc.contributor.departmentCOMPUTATIONAL SCIENCE
dc.description.doi10.1016/j.neucom.2007.07.025
dc.description.sourcetitleNeurocomputing
dc.description.volume71
dc.description.issue4-6
dc.description.page576-583
dc.description.codenNRCGE
dc.identifier.isiut000253663800015
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