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ToolsHanna, A. I., Mandic, D. P. and Razaz, M. (2001) A normalised backpropagation learning algorithm for multilayer feed-forward neural adaptive filters. In: 2001 IEEE Signal Processing Society Workshop on Neural Nets for Signal Processing XI, 2001-09-10 - 2001-09-12.
Full text not available from this repository.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 |
| Depositing User: | Vishal Gautam |
| Date Deposited: | 18 Aug 2011 11:43 |
| Last Modified: | 15 Dec 2022 01:06 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/22441 |
| DOI: | 10.1109/NNSP.2001.943111 |
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