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https://doi.org/10.1016/j.neucom.2007.07.025
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 ELM Feedforward networks I-ELM |
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 |
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