A normalised backpropagation learning algorithm for multilayer feed-forward neural adaptive filters

Hanna, A. I., Mandic, D. P. and Razaz, M. (2001) A normalised backpropagation learning algorithm for multilayer feed-forward neural adaptive filters. In: Proceedings of the 2001 IEEE Signal Processing Society Workshop on Neural Nets for Signal Processing XI, 2001-09-10 - 2001-09-12.

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Abstract

Analysis of a normalised backpropagation (NBP) algorithm employed in feed-forward multilayer nonlinear adaptive filters trained by backpropagation is provided. It is first shown that a degree of freedom in training of a nonlinear adaptive filter can be removed according to the relationship between the gain of the activation function, learning rate and weight matrix. The derivation of the NBP algorithm for a multilayer feed-forward neural adaptive filter is then provided based upon the minimisation of the instantaneous output error of the filter. Simulation results show that the NBP algorithm converges faster than a standard backpropagation algorithm and achieves better prediction gain when applied to nonlinear and non-stationary signals

Item Type: Conference or Workshop Item (Paper)
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: Vishal Gautam
Date Deposited: 18 Aug 2011 11:43
Last Modified: 01 May 2020 00:14
URI: https://ueaeprints.uea.ac.uk/id/eprint/22441
DOI: 10.1109/NNSP.2001.943111

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