Please use this identifier to cite or link to this item:
|Title:||Incremental extreme learning machine with fully complex hidden nodes||Authors:||Huang, G.-B.
Complex activation function
|Issue Date:||2008||Citation:||Huang, 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||Abstract:||Huang 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.||Source Title:||Neurocomputing||URI:||http://scholarbank.nus.edu.sg/handle/10635/39831||ISSN:||09252312||DOI:||10.1016/j.neucom.2007.07.025|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.