Recursive DLS solution for extreme learning machine-based channel equalizer

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...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2008, Vol.71 (4), p.592-599
Main Author: Lim, JunSeok
Format: Article
Language:English
Subjects:
Channel equalizer
Extreme learning machine
Recursive data least squares
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Summary: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.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2007.07.022