A fully adaptive normalized nonlinear gradient descent algorithm for nonlinear system identification

@article{Krcmar2001AFA,
  title={A fully adaptive normalized nonlinear gradient descent algorithm for nonlinear system identification},
  author={Igor R. Krcmar and Danilo P. Mandic},
  journal={2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221)},
  year={2001},
  volume={6},
  pages={3493-3496 vol.6}
}
A fully adaptive normalized nonlinear gradient descent (FANNGD) algorithm for neural adaptive filters employed for nonlinear system identification is proposed. This full adaptation is achieved using the instantaneous squared prediction error to adapt the free parameter of the NNGD algorithm. The convergence analysis of the proposed algorithm is undertaken using the contractivity property of the nonlinear activation function of a neuron. Simulation results show that a fully adaptive NNGD… CONTINUE READING

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