Đang chuẩn bị liên kết để tải về tài liệu:
EURASIP Journal on Applied Signal Processing 2003:12, 1229–1237 c 2003 Hindawi Publishing

EURASIP Journal on Applied Signal Processing 2003:12, 1229–1237 c 2003 Hindawi Publishing Corporation Nonlinear System Identification Using Neural Networks Trained with Natural Gradient Descent Mohamed Ibnkahla Electrical and Computer Engineering Department, Queen’s University, Kingston, Ontario, Canada K7L 3N6 Email: mohamed.ibnkahla@ece.queensu.ca Received 13 December 2002 and in revised form 17 May 2003 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 H followed by a zero-memory nonlinearity g(·). The NN model is composed of a linear adaptive filter Q followed by a two-layer memoryless nonlinear NN | EURASIP Journal onApplied Signal Processing 2003 12 1229-1237 2003 Hindawi Publishing Corporation Nonlinear System Identification Using Neural Networks Trained with Natural Gradient Descent Mohamed Ibnkahla Electrical and Computer Engineering Department Queen s University Kingston Ontario Canada K7L 3N6 Email mohamed.ibnkahla@ece.queensu.ca Received 13 December 2002 and in revised form 17 May 2003 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 H followed by a zero-memory nonlinearity g . The NN model is composed of a linear adaptive filter Q 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. Keywords and phrases satellite communications system identification adaptive signal processing neural networks. 1. INTRODUCTION Most techniques that have been proposed for nonlinear system identification are based on parametrized nonlinear models such as Wiener and Hammerstein models 1 2 3 4 Volterra series 5 wavelet networks 3 neural networks NNs 6 7 and so forth. The estimation of the parameters is performed either using nonadaptive techniques such as least squares methods and higher-order statistics-based methods 4 8 9 10 11 or adaptive techniques such as the backpropagation BP algorithm 12 13 14 and online learning 3 15 . NN approaches for modeling and identifying nonlinear dynamical systems have shown excellent performance compared to classical techniques 1 6 9 13 16 . NNs trained with the BP algorithm 14 16 have however two major drawbacks first their convergence is slow which can be inadequate for online training second the NN .