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Nonlinear System Identification Using Neural Networks Trained with Natural Gradient Descent
EURASIP Journal on Advances in Signal Processing volume 2003, Article number: 574805 (2003)
Abstract
We use natural gradient (NG) learning neural networks (NNs) for modeling and identifying nonlinear systems with memory. The nonlinear system is comprised of a discrete-time linear filter followed by a zero-memory nonlinearity. The NN model is composed of a linear adaptive filter followed by a two-layer memoryless nonlinear NN. A Kalman filter-based technique and a search-and-converge method have been employed for the NG algorithm. It is shown that the NG descent learning significantly outperforms the ordinary gradient descent and the Levenberg-Marquardt (LM) procedure in terms of convergence speed and mean squared error (MSE) performance.
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Ibnkahla, M. Nonlinear System Identification Using Neural Networks Trained with Natural Gradient Descent. EURASIP J. Adv. Signal Process. 2003, 574805 (2003). https://doi.org/10.1155/S1110865703306079
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DOI: https://doi.org/10.1155/S1110865703306079