| Summary: | Inference measurements with time-delayed feed-forward neural networks facilitates the
inference of unknown variables from known variables in non-linear dynamic systems. This
is based only on the mapping data of the known variable and variable to be inferred. For
successful inference, several constraints have to be overcome. This is, the neural network
should have the correct topology, the training data set characteristics must have inherent
attributes to ensure generalisation and the training algorithm must be capable of finding
an acceptable local minimum on the error surface. At present, the neural network topology
is based on trial and error, while the generalisation capability of the trained neural network
is tested by using test and validation sets.
Due to the lack of design methods for the topology of neural networks and the need
for independent testing and validation, this thesis endeavours to develop a generalised
method to find the optimum topology for accurate inference measurements. The aim is
further to develop a method for judging the training set that could lead to generalisation
without using test sets or validation sets. For this to be done, the training algorithm
should succeed in finding a small enough local minimum on the error surface.
The developed methods are applied to a simulated model of the pebble bed modular
reactor (PBMR). === Thesis (Ph.D. (Electronical Engineering)--North-West University, Potchefstroom Campus, 2004.
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