Application of backpropagation-like generative algorithms to various problems.

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dc.contributor.advisor Sartori-Angus, Alan G.
dc.creator Powell, Alan Roy.
dc.date.accessioned 2012-06-27T13:43:19Z
dc.date.available 2012-06-27T13:43:19Z
dc.date.created 1992
dc.date.issued 1992
dc.identifier.uri http://hdl.handle.net/10413/5619
dc.description Thesis (M.Sc.)-University of Natal, Durban, 1992. en
dc.description.abstract Artificial neural networks (ANNs) were originally inspired by networks of biological neurons and the interactions present in networks of these neurons. The recent revival of interest in ANNs has again focused attention on the apparent ability of ANNs to solve difficult problems, such as machine vision, in novel ways. There are many types of ANNs which differ in architecture and learning algorithms, and the list grows annually. This study was restricted to feed-forward architectures and Backpropagation- like (BP-like) learning algorithms. However, it is well known that the learning problem for such networks is NP-complete. Thus generative and incremental learning algorithms, which have various advantages and to which the NP-completeness analysis used for BP-like networks may not apply, were also studied. Various algorithms were investigated and the performance compared. Finally, the better algorithms were applied to a number of problems including music composition, image binarization and navigation and goal satisfaction in an artificial environment. These tasks were chosen to investigate different aspects of ANN behaviour. The results, where appropriate, were compared to those resulting from non-ANN methods, and varied from poor to very encouraging. en
dc.language.iso en_ZA en
dc.subject Neural networks (Computer science) en
dc.subject Np-Complete problems. en
dc.subject Algorithms. en
dc.subject Theses--Computer science. en
dc.title Application of backpropagation-like generative algorithms to various problems. en
dc.type Thesis en

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