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Channel Equalization Using Complex Extreme Learning Machine with RBF Kernels

  • Ming-Bin Li
  • Guang-Bin Huang
  • Paramasivan Saratchandran
  • Narasimhan Sundararajan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

Abstract

This paper studies the performance of extreme learning machine with complex-valued radial basis function (ELM-CRBF) in the channel equalization applications. Comparing with complex minimal resource allocation network (CMRAN), complex radial basis function (CRBF) network and Bayesian equalizers, the simulation results show that ELM-CRBF equalizer is superior in terms of symbol error rate (SER) and learning speed.

Keywords

Radial Basis Function Extreme Learning Machine Hide Neuron Radial Basis Function Neural Network Radial Basis Function Network 
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

  • Ming-Bin Li
    • 1
  • Guang-Bin Huang
    • 1
  • Paramasivan Saratchandran
    • 1
  • Narasimhan Sundararajan
    • 1
  1. 1.School of Electrical and Electronic EngineeringNanyang Technological UniversitySingapore

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