Recursive complex extreme learning machine with widely linear processing for nonlinear channel equalizer

Authors:
Junseok Lim;Jaejin Jeon;Sangwook Lee
Affiliations:
Dept. of Electronics Eng., Sejong University, Seoul, Korea;Samsung Electronics Co.,Ltd;LG Electronics Inc
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

Citing 5
Cited 2

Adaptive filter theory (3rd ed.)

Adaptive filter theory (3rd ed.)
Letters: Fully complex extreme learning machine

Neurocomputing
Widely linear estimation with complex data

IEEE Transactions on Signal Processing
Channel equalization using adaptive complex radial basis function networks

IEEE Journal on Selected Areas in Communications
Classification ability of single hidden layer feedforward neural networks

IEEE Transactions on Neural Networks

Recursive DLS solution for extreme learning machine-based channel equalizer

Neurocomputing
Partitioned online sequential extreme learning machine for large ordered system modeling

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recently, a new learning algorithm for the feedforward neural network named the complex extreme learning machine (C-ELM) which can give better performance than traditional tuning-based learning methods for feedforward neural networks in terms of generalization and learning speed has been proposed by Huang et al. In this paper, we propose a new widely linear recursive C-ELM algorithm for nonlinear channel equalizer. The proposed algorithm improves its performance especially in case of real valued modulation such as BPSK and PAM. The computer simulation results demonstrate the improvement in performance achievable with the proposed equalization algorithm.