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

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 con...

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Bibliographic Details
Main Authors: Krcmar, I.R., Mandic, D.P.
Format: Conference Proceeding
Language:English
Subjects:
Adaptive filters
Algorithm design and analysis
Convergence
Cost function
Education
Information systems
Neurons
Nonlinear equations
Nonlinear systems
System identification
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Summary: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.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2001.940594