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

A. I. Hanna, D. P. Mandic, M. Razaz

Research output: Contribution to conferencePaper

8 Citations (Scopus)

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
Original languageEnglish
Pages63-72
Number of pages10
DOIs
Publication statusPublished - Sep 2001
EventProceedings of the 2001 IEEE Signal Processing Society Workshop on Neural Nets for Signal Processing XI - North Falmouth, MA, United States
Duration: 10 Sep 200112 Sep 2001

Conference

ConferenceProceedings of the 2001 IEEE Signal Processing Society Workshop on Neural Nets for Signal Processing XI
CountryUnited States
CityNorth Falmouth, MA
Period10/09/0112/09/01

Cite this