Fast nonlinear channel equalisation using generalised diagonal recurrent neural networks

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A generalised diagonal recurrent neural network (GDRNN) for nonlinear channel equalisation is proposed. The hidden nodes of the GDRNN have recurrent weights to capture the dynamic characteristics of the communication channels. The learning algorithm of the proposed GDRNN is derived, based on constrained optimisation. The proposed neural network gives faster learning speed and has better convergence properties than do conventional channel equalisers.
Publisher
IEE-INST ELEC ENG
Issue Date
1998-11
Language
English
Article Type
Article
Citation

ELECTRONICS LETTERS, v.34, no.23, pp.2253 - 2255

ISSN
0013-5194
URI
http://hdl.handle.net/10203/75596
Appears in Collection
EE-Journal Papers(저널논문)
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