This paper applies natural gradient (NG) learning neural networks (NNs) for modeling and identification of nonlinear systems with memory. The nonlinear system is comprised of a discrete-time linear filter H followed by a zero-memory nonlinearity g(.). The neural network model is composed of a linear adaptive filter Q and a two-layer nonlinear neural network (NN). It is shown that the NG learning method outperforms the ordinary gradient descent method in terms of convergence speed and mean squared error (MSE) performance.

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Conference 2002 IEEE International Conference on Acustics, Speech, and Signal Processing
Citation
Ibnkahla, M, & Pochon, B. (Benoit). (2002). Natural gradient learning neural networks for modeling and identification of nonlinear systems with memory. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings.