This thesis presents a study of neural network representation and
behaviour. The study places neural networks in the context of designing
reliable systems. Several new results on network size and topology are
Knowledge based training of neural networks is examined. This is
essential for designing reliable neural systems in which the
subsymbolic reasoning processes are well defined. Sandwich nodes are
introduced and studied as atomic knowledge elements in neural
networks. Two new network architectures are introduced, the
Loughborough Net and the Loughborough Control Net. These make use of
the parallelism inherent in sandwich node representations.
The interpretation of neural network representations as logical
transformations and rule systems are presented. An equivalence of the
rule systems and neural network representation is proposed and
discussed. This equivalence is required in order that the total behaviour
of the neural network can be understood.
A new methodology for designing reliable neural network systems
making use of knowledge based training is proposed. This is used to
present a general design methodology for the construction of. reliable
neural network control systems using the Loughborough Control Net
architecture. A case study is discussed where the methodology was
applied to the design of an adhesive dispensing controller.
A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University.