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Title: On nonlinear modular neural filters
Authors: Chen, Mo
Mandic, Danilo P.
Gautama, Temujin
Van Hulle, Marc #
Issue Date: 2005
Host Document: vol:V pages:317-320
Conference: International Conference on Acoustics, Speech and Signal Processing (ICASSP) location:Philadelphia, USA date:19-23 March 2005
Abstract: An assessment of the performance of the pipelined recurrent neural
network (PRNN) is provided from two aspects, a quantitative
one based on the prediction gain and a qualitative one based on examining
the changes in the nature of the processed signal. This is
achieved by means of the recently introduced ‘Delay Vector Variance’
(DVV) method for phase space signal characterisation. A
comprehensive analysis of this approach on both linear and nonlinear
benchmark signals suggests that the PRNN not only outperforms
a single recurrent neural network (RNN) in terms of the
prediction gain but also has better or similar performance in terms
of preserving the nature of the processed signal.
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Research Group Neurophysiology
# (joint) last author

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