Neural network equalizer

Chulhee Lee; Jinwook Go; Byungjoon Baek; Hyunsoo Choi

doi:10.1007/11816157_20

Neural network equalizer

Chulhee Lee, Jinwook Go, Byungjoon Baek, Hyunsoo Choi

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

In this paper, we view equalization as a multi-class classification problem and use neural networks to detect binary signals in the presence of noise and interference. In particular, we compare the performance of a recently published training algorithm, a multi-gradient, with that of the conventional back-propagation. Then, we apply a feature extraction to obtain more efficient neural networks. Experiments show that neural network equalizers which view equalization as multi-class problems provide significantly improved performance compared to the conventional LMS algorithm while the decision boundary feature extraction method significantly reduces the complexity of the network.

Original language	English
Title of host publication	International Conference on Intelligent Computing, ICIC 2006, Proceedings
Publisher	Springer Verlag
Pages	204-215
Number of pages	12
ISBN (Print)	3540372717, 9783540372714
DOIs	https://doi.org/10.1007/11816157_20
Publication status	Published - 2006
Event	International Conference on Intelligent Computing, ICIC 2006 - Kunming, China Duration: 2006 Aug 16 → 2006 Aug 19

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4113 LNCS - I
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	International Conference on Intelligent Computing, ICIC 2006
Country/Territory	China
City	Kunming
Period	06/8/16 → 06/8/19

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/11816157_20

Cite this

Lee, C., Go, J., Baek, B., & Choi, H. (2006). Neural network equalizer. In International Conference on Intelligent Computing, ICIC 2006, Proceedings (pp. 204-215). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4113 LNCS - I). Springer Verlag. https://doi.org/10.1007/11816157_20

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title = "Neural network equalizer",

abstract = "In this paper, we view equalization as a multi-class classification problem and use neural networks to detect binary signals in the presence of noise and interference. In particular, we compare the performance of a recently published training algorithm, a multi-gradient, with that of the conventional back-propagation. Then, we apply a feature extraction to obtain more efficient neural networks. Experiments show that neural network equalizers which view equalization as multi-class problems provide significantly improved performance compared to the conventional LMS algorithm while the decision boundary feature extraction method significantly reduces the complexity of the network.",

author = "Chulhee Lee and Jinwook Go and Byungjoon Baek and Hyunsoo Choi",

year = "2006",

doi = "10.1007/11816157_20",

language = "English",

isbn = "3540372717",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "204--215",

booktitle = "International Conference on Intelligent Computing, ICIC 2006, Proceedings",

address = "Germany",

note = "International Conference on Intelligent Computing, ICIC 2006 ; Conference date: 16-08-2006 Through 19-08-2006",

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Lee, C, Go, J, Baek, B & Choi, H 2006, Neural network equalizer. in International Conference on Intelligent Computing, ICIC 2006, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4113 LNCS - I, Springer Verlag, pp. 204-215, International Conference on Intelligent Computing, ICIC 2006, Kunming, China, 06/8/16. https://doi.org/10.1007/11816157_20

Neural network equalizer. / Lee, Chulhee; Go, Jinwook; Baek, Byungjoon et al.
International Conference on Intelligent Computing, ICIC 2006, Proceedings. Springer Verlag, 2006. p. 204-215 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4113 LNCS - I).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Neural network equalizer

AU - Lee, Chulhee

AU - Go, Jinwook

AU - Baek, Byungjoon

AU - Choi, Hyunsoo

PY - 2006

Y1 - 2006

N2 - In this paper, we view equalization as a multi-class classification problem and use neural networks to detect binary signals in the presence of noise and interference. In particular, we compare the performance of a recently published training algorithm, a multi-gradient, with that of the conventional back-propagation. Then, we apply a feature extraction to obtain more efficient neural networks. Experiments show that neural network equalizers which view equalization as multi-class problems provide significantly improved performance compared to the conventional LMS algorithm while the decision boundary feature extraction method significantly reduces the complexity of the network.

AB - In this paper, we view equalization as a multi-class classification problem and use neural networks to detect binary signals in the presence of noise and interference. In particular, we compare the performance of a recently published training algorithm, a multi-gradient, with that of the conventional back-propagation. Then, we apply a feature extraction to obtain more efficient neural networks. Experiments show that neural network equalizers which view equalization as multi-class problems provide significantly improved performance compared to the conventional LMS algorithm while the decision boundary feature extraction method significantly reduces the complexity of the network.

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U2 - 10.1007/11816157_20

DO - 10.1007/11816157_20

M3 - Conference contribution

AN - SCOPUS:33749580268

SN - 3540372717

SN - 9783540372714

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 204

EP - 215

BT - International Conference on Intelligent Computing, ICIC 2006, Proceedings

PB - Springer Verlag

T2 - International Conference on Intelligent Computing, ICIC 2006

Y2 - 16 August 2006 through 19 August 2006

ER -

Neural network equalizer

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