A posteriori error learning in nonlinear adaptive filters

Mandic, D. P. and Chambers, J. A. (1999) A posteriori error learning in nonlinear adaptive filters. IEE Proceedings: Vision, Image and Signal Processing, 146 (6). pp. 293-296. ISSN 1350-245X

Full text not available from this repository.

Abstract

The authors provide relationships between the a priori and a posteriori errors of adaptation algorithms for real-time output-error nonlinear adaptive filters realised as feedforward or recurrent neural networks. The analysis is undertaken for a general nonlinear activation function of a neuron, and for gradient-based learning algorithms, for both a feedforward (FF) and recurrent neural network (RNN). Moreover, the analysis considers both contractive and expansive forms of the nonlinear activation functions within the networks. The relationships so obtained provide the upper and lower error bounds for general gradient based a posteriori learning in neural networks

Item Type: Article
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: Vishal Gautam
Date Deposited: 08 Mar 2011 08:36
Last Modified: 21 Apr 2020 21:21
URI: https://ueaeprints.uea.ac.uk/id/eprint/23720
DOI: 10.1049/ip-vis:19990742

Actions (login required)

View Item View Item