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

Authors:
L.S.H. Ngia;J. Sjoberg
Affiliations:
Dept. of Signals & Syst., Chalmers Univ. of Technol., Goteborg;-
Venue:
IEEE Transactions on Signal Processing
Year:
2000

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Abstract

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 algorithms are pointed out. The algorithms are tested on some examples, and it is shown that generally the recursive Levenberg-Marquardt algorithm has better convergence properties than the other algorithms