In this thesis the two disciplines of Statistics and Artificial Neural Networks
are combined into an integrated study of a data set of a weather modification
Experiment.
An extensive literature study on artificial neural network methodology has
revealed the strongly interdisciplinary nature of the research and the applications
in this field.
An artificial neural networks are becoming increasingly popular with data
analysts, statisticians are becoming more involved in the field. A recursive
algoritlun is developed to optimize the number of hidden nodes in a feedforward
artificial neural network to demonstrate how existing statistical techniques
such as nonlinear regression and the likelihood-ratio test can be applied in
innovative ways to develop and refine neural network methodology.
This pruning algorithm is an original contribution to the field of artificial
neural network methodology that simplifies the process of architecture selection,
thereby reducing the number of training sessions that is needed to find
a model that fits the data adequately.
[n addition, a statistical model to classify weather modification data is developed
using both a feedforward multilayer perceptron artificial neural network
and a discriminant analysis. The two models are compared and the effectiveness
of applying an artificial neural network model to a relatively small
data set assessed.
The formulation of the problem, the approach that has been followed to
solve it and the novel modelling application all combine to make an original
contribution to the interdisciplinary fields of Statistics and Artificial Neural
Networks as well as to the discipline of meteorology. / Mathematical Sciences / D. Phil. (Statistics)
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:unisa/oai:uir.unisa.ac.za:10500/600 |
| Date | 30 June 2002 |
| Creators | Fletcher, Lizelle |
| Contributors | Steffens, F. E. (Francois Eliza), Katkovnik, V. |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 1 online resource (xiii, 207 leaves) |
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