Title: Learning neural networks with noisy inputs using the errors-in-variables approach
Authors: Van Gorp, J ×
Schoukens, Joannes
Pintelon, R #
Issue Date: Mar-2000
Publisher: Ieee-inst electrical electronics engineers inc
Series Title: IEEE Transactions on neural networks vol:11 issue:2 pages:402-414
Abstract: Currently, most Learning algorithms for neural-network modeling are based on the output error approach, using a least squares cost function. This method provides good results when the network is trained with noisy output data and known inputs. Special care must be taken, however, when training the network with noisy input data, or when both inputs and outputs contain noise, This paper proposes a novel cost function for learning NN with noisy inputs, based on the errors-in-variables stochastic framework, A learning scheme is presented and examples are given demonstrating the improved performance in neural-network curve fitting, at the cost of increased computation time.
ISSN: 1045-9227
Publication status: published
KU Leuven publication type: IT
Appears in Collections:ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics
× corresponding author
# (joint) last author

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