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Statistical modelling by neural networks

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dc.contributor.advisor Steffens, F. E. (Francois Eliza)
dc.contributor.advisor Katkovnik, V. Fletcher, Lizelle 2009-08-25T10:45:05Z 2009-08-25T10:45:05Z 2002-06 2002-06-30
dc.identifier.citation Fletcher, Lizelle (2002) Statistical modelling by neural networks, University of South Africa, Pretoria, <> en
dc.description.abstract 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. en
dc.format.extent 1 online resource (xiii, 207 leaves) en
dc.language.iso en
dc.subject Statistical modelling en
dc.subject Artificial neural networks en
dc.subject Nonlinear regression en
dc.subject Multilayer perceptron en
dc.subject Backpropagation en
dc.subject Hidden nodes en
dc.subject Pruning algorithm en
dc.subject Classification en
dc.subject Discriminant analysis en
dc.subject Analysis of variance en
dc.subject Weather modification en
dc.subject.ddc 006.320727
dc.subject.lcsh Neural networks (Computer science) -- Statistical methods en
dc.subject.lcsh Weather control -- Statistical methods en
dc.title Statistical modelling by neural networks en
dc.type Thesis en
dc.description.department Mathematical Sciences en D. Phil. (Statistics)

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