Improvements of Analog Neural Networks Based on Kalman Filter
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Date
2002-04
Authors
ORCID
Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
Společnost pro radioelektronické inženýrství
Abstract
In the paper, original improvements of recurrent analog neural networks, which are based on Kalman filter, are presented. These improvements eliminate some disadvantages of the classical Kalman neural network and enable a real time processing of quickly changing signals, which appear in adaptive antennas and similar applications. This goal is reached using such circuit elements, which increase the convergence rate of the network and decrease the dependence of convergence rate on the ratio of eigenvalues of the correlation matrix of input signals.
Description
Citation
Radioengineering. 2002, vol. 11, č. 1, s. 6-13. ISSN 1210-2512
http://www.radioeng.cz/fulltexts/2002/02_01_06_13.pdf
http://www.radioeng.cz/fulltexts/2002/02_01_06_13.pdf
Document type
Peer-reviewed
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Published version
Date of access to the full text
Language of document
en