Natural gradient learning neural networks for modeling and identification of nonlinear systems with memory
Mohamed A. Ibnkahla, Benoit Pochon · IEEE International Conference on Acoustics Speech and Signal Processing · 2002
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.