Recursive Complex Extreme Learning Machine with Widely Linear Processing for Nonlinear Channel Equalizer

  • Junseok Lim
  • Jaejin Jeon
  • Sangwook Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


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.


Hide Neuron Feedforward Neural Network Linear Processing Recursive Less Square Channel Equalization 
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

  • Junseok Lim
    • 1
  • Jaejin Jeon
    • 2
  • Sangwook Lee
    • 3
  1. 1.Dept. of Electronics Eng.Sejong UniversitySeoulKorea
  2. 2.Samsung Electronics Co.,Ltd 
  3. 3.LG Electronics Inc 

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