Incremental extreme learning machine with fully complex hidden nodes

نویسندگان

  • Guang-Bin Huang
  • Ming-Bin Li
  • Lei Chen
  • Chee Kheong Siew
چکیده

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. r 2007 Elsevier B.V. All rights reserved.

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عنوان ژورنال:
  • Neurocomputing

دوره 71  شماره 

صفحات  -

تاریخ انتشار 2008