Recursive DLS solution for extreme learning machine-based channel equalizer

Jun-Seok Lim

doi:10.1016/j.neucom.2007.07.022

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Recursive DLS solution for extreme learning machine-based channel equalizer

2008-01-01

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Jun-Seok Lim *

DOI：10.1016/j.neucom.2007.07.022

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Abstract

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Recently, a new learning algorithm for a single-hidden-layer feedforward neural network (SLFN), named the complex extreme learning machine (C-ELM), has. been proposed in Li et al. [Fully complex extreme learning machine, Neurocomputing 68 (2005) 306-314]. Although it shows potential applicability in many areas, there is still room for improvement in performance, especially in training-based equalization applications in which the noise is only within the received data. In this paper, we propose a new solution applying the data least squares (DLS) method. Simulations show that DLS-based C-ELM outperforms the ordinary-least-square-based one in channel equalization problems. (c) 2007 Elsevier B.V. All rights reserved.

Keywords:

extreme learning machine

recursive data least squares

channel equalizer

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