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Incremental extreme learning machine with fully complex hidden nodes
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DOI：10.1016/j.neucom.2007.07.025.png)
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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. (c) 2007 Elsevier B.V. All rights reserved.
Keyword:
feedforward networks
complex activation function
constructive networks
ELM
I-ELM
channel equalization
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IF:
6.5
论文数:
2.5W
被引数:
6.5W
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