Neural Network Equalizer

  • Chulhee Lee
  • Jinwook Go
  • Byungjoon Baek
  • Hyunsoo Choi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)


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.


Classification Accuracy Less Mean Square Decision Boundary Less Mean Square Algorithm Intersymbol Interference 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Chulhee Lee
    • 1
  • Jinwook Go
    • 1
  • Byungjoon Baek
    • 1
  • Hyunsoo Choi
    • 1
  1. 1.Dept. Electrical and Electronic EngineeringYonsei UniversitySeoulSouth Korea

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