  This paper compares two neural network input selection schemes, the Principal
Component Analysis (PCA) and the Automatic Relevance Determination (ARD) based
on Mac-Kay's evidence framework. The PCA takes all the input data and projects
it onto a lower dimension space, thereby reduc-ing the dimension of the input
space. This input reduction method often results with parameters that have
significant influence on the dynamics of the data being diluted by those that
do not influence the dynamics of the data. The ARD selects the most relevant
input parameters and discards those that do not contribute significantly to the
dynamics of the data being modelled. The ARD sometimes results with important
input parameters being discarded thereby compromising the dynamics of the data.
The PCA and ARD methods are implemented together with a Multi-Layer-Perceptron
(MLP) network for fault identification in structures and the performance of the
two methods is as-sessed. It is observed that ARD and PCA give similar
accu-racy levels when used as input-selection schemes. There-fore, the choice
of input-selection scheme is dependent on the nature of the data being
processed.
