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Title: Incremental extreme learning machine with fully complex hidden nodes
Authors: Huang, G.-B.
Li, M.-B.
Chen, L. 
Siew, C.-K.
Keywords: Channel equalization
Complex activation function
Constructive networks
Feedforward networks
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.
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
ISSN: 09252312
DOI: 10.1016/j.neucom.2007.07.025
Appears in Collections:Staff Publications

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