A feed forward architecture is suggested that increases the complexity of conventional neural
network components through the implementation of a more complex scheme of interconnection.
This is done with a view to increasing the range of application of the feed forward paradigm.
The uniqueness of this new network design is illustrated by developing an extended taxonomy
of accepted published constructs specific and similar to the higher order, product kernel
approximations achievable using "parallel paths". Network topologies from this taxonomy are
then compared to each other and the architectures containing parallel paths. In attempting this
comparison, the context of the term "network topology" is reconsidered.
The output of "channels" in these parallel paths are the products of a conventional connection
as observed facilitating interconnection between two layers in a multilayered perceptron and the
output of a network processing unit, a "control element", that can assume the identity of a
number of pre-existing processing paradigms.
The inherent property of universal approximation is tested by existence proof and the method
found to be inconclusive. In so doing an argument is suggested to indicate that the parametric
nature of the functions as determined by conditions upon initialization may only lead to
conditional approximations. The property of universal approximation is neither, confirmed or
denied. Universal approximation cannot be conclusively determined by the application of Stone
Weierstrass Theorem, as adopted from real analysis.
This novel implementation requires modifications to component concepts and the training
algorithm. The inspiration for these modifications is related back to previously published work
that also provides the basis of "proof of concept".
By achieving proof of concept the appropriateness of considering network topology without
assessing the impact of the method of training on this topology is considered and discussed in
some detail.
Results of limited testing are discussed with an emphasis on visualising component
contributions to the global network output.
| Identifer | oai:union.ndltd.org:ADTP/216632 |
| Date | January 2006 |
| Creators | Murray, Andrew Gerard William, n/a |
| Publisher | Swinburne University of Technology. |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | http://www.swin.edu.au/), Copyright Andrew Gerard William Murray |
Page generated in 0.0061 seconds