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Paper   IPM / Cognitive / 7858
School of Cognitive Sciences
  Title:   Adaptive Channel Equalization using Fasteuclidean Direction Search Algorithm
1.  M. Shams Esfand Abadi
2.  A. Mahlooji Far
3.  E. Kabir
4.  R. Ebrahimpour
  Status:   In Proceedings
  Proceeding: 2nd IEEE-GCC Conference
  Year:  2004
  Supported by:  IPM
Least mean square (LMS) adaptive filters have been used in a wide range of signal processing applications because of its simplicity in computation and implementation. The Recursive Least Squares (RLS) algorithm has established itself as the ?ultimate? adaptive filtering algorithm in the sense that it is the adaptive filter exhibiting the best convergence behavior. Unfortunately, practical implementations of the algorithm are often associated with high computational complexity and/or poor numerical properties. Recently adaptive filtering are presented that are based on the Euclidean Direction Search (EDS) method of optimization. The fast version of this class is called the Fast-EDS or FEDS algorithm. The FEDS based algorithms have a fast convergence rate and computational complexity. This algorithm is investigated for adaptive channel equalization. The FEDS algorithm is shown to perform very well in attenuating noise and intersymbol interference

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