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

  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},
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
Highly Influential
This paper has highly influenced 11 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 198 citations. REVIEW CITATIONS


Publications citing this paper.
Showing 1-10 of 74 extracted citations

Adaptive Regularizer for Recursive Neural Network Training Algorithms

2008 11th IEEE International Conference on Computational Science and Engineering - Workshops • 2008
View 10 Excerpts
Highly Influenced

Bitrate-maximizing time-domain equalizer design for DMT-based systems

IEEE Transactions on Communications • 2003
View 4 Excerpts
Highly Influenced

Recursive Bayesian Recurrent Neural Networks for Time-Series Modeling

IEEE Transactions on Neural Networks • 2010
View 4 Excerpts
Highly Influenced

199 Citations

Citations per Year
Semantic Scholar estimates that this publication has 199 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 31 references

Theory and Practice of Recursive Identification

L. Ljung, T. Söderström
Cambridge, MA: MIT Press • 1983
View 9 Excerpts
Highly Influenced

Linearized adaptive filter for a nonlinear system using neural network based on the extended Kalman filter,

H. Kinjo, S. Tamaki, T. Yamamoto
Trans. IEE Jpn., • 1997

Separable nonlinear least-squares minimization—Possible improvements for neural net fitting,

J. Sjöberg, M. Viberg
in Proc. IEEE Workshop Neural Networks Signal Process., • 1997
View 1 Excerpt

Adaptive Filter Theory

S. Haykin
Upper Saddle River, NJ: Prentice- Hall • 1996

and W

Q. J. Zhang, Y. J. Zhang
Ye, “Local-sparse connection multilayer networks,” in Proc. IEEE Int. Conf. Neural Networks • 1995
View 1 Excerpt

Similar Papers

Loading similar papers…