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

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.