Efficient training of neural nets for nonlinear adaptive filtering using a recursive Levenberg-Marquardt algorithm

@article{Ngia2000EfficientTO,
  title={Efficient training of neural nets for nonlinear adaptive filtering using a recursive Levenberg-Marquardt algorithm},
  author={Lester S. H. Ngia and Jonas Sj{\"o}berg},
  journal={IEEE Trans. Signal Processing},
  year={2000},
  volume={48},
  pages={1915-1927}
}
The Levenberg—Marquardt algorithm is often superior to other training algorithms in off-line applications. This motivates the proposal of using a recursive version of the algorithm for on-line training of neural nets for nonlinear adaptive filtering. The performance of the suggested algorithm is compared with other alternative recursive algorithms, such as the recursive version of the off-line steepest-descent and Gauss—Newton algorithms. The advantages and disadvantages of the different… CONTINUE READING
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