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

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2 Author(s)
Krcmar, Igor R. ; Fac. of Electr. Eng., Banjaluka Univ., Bosnia-Herzegovina ; Mandic, D.P.

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 algorithm outperforms the standard NNGD algorithm for nonlinear system identification

Published in:

Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP '01). 2001 IEEE International Conference on  (Volume:6 )

Date of Conference:

2001

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