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A Hybrid Genetic Algorithm with Simulated Annealing for Nonlinear Blind Equalization Using RBF Networks

  • Soowhan Han
  • Imgeun Lee
  • Changwook Han
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4431)

Abstract

In this study, a hybrid genetic algorithm, which merges a genetic algorithm with simulated annealing, is derived for nonlinear channel blind equalization using RBF networks. The proposed hybrid genetic algorithm is used to estimate the output states of a nonlinear channel, based on the Bayesian likelihood fitness function, instead of the channel parameters. From these estimated output states, the desired channel states of the nonlinear channel are derived and placed at the center of a RBF equalizer to reconstruct transmitted symbols. In the simulations, binary signals are generated at random with Gaussian noise. The performance of the proposed method is compared with those of a conventional genetic algorithm (GA) and a simplex GA. It is shown that the relatively high accuracy and fast convergence speed have been achieved.

Keywords

Hybrid Genetic Algorithm Transmitted Symbol Normalize Root Mean Square Error Volterra Kernel Decision Feedback Equalizer 
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 Berlin Heidelberg 2007

Authors and Affiliations

  • Soowhan Han
    • 1
  • Imgeun Lee
    • 1
  • Changwook Han
    • 2
  1. 1.Department of Multimedia Engineering, Dongeui University, Busan,614-714Korea
  2. 2.School of Electrical Eng. and Computer Science, Yeungnam University,712-749Korea

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