Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.513549
Title: Using orthogonal arrays to train artificial neural networks
Author: Viswanathan, Alagappan
Awarding Body: Robert Gordon University
Current Institution: Robert Gordon University
Date of Award: 2005
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Abstract:
The thesis outlines the use of Orthogonal Arrays for the training of Artificial Neural Networks. Such arrays are popularly used in system optimisation and are known as Taguchi Methods. The chief advantage of the method is that the network can learn quickly. Fast training methods may be used in certain Control Systems and it has been suggested that they could find application in ‘disaster control,’ where a potentially dangerous system (for example, suffering a mechanical failure) needs to be controlled quickly. Previous work on the methods has shown that they suffer problems when used with multi-layer networks. The thesis discusses the reasons for these problems and reports on several successful techniques for overcoming them. These techniques are based on the consideration of the neuron, rather then the individual weight, as a factor to be optimised. The applications of technique and further work are also discussed.
Supervisor: Maxwell, Grant M. ; MacLeod, Christopher ; Reddipogu, Ann Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Al
EThOS ID: uk.bl.ethos.513549  DOI: Not available
Keywords: Orthogonal arrays ; Artificial neural networks ; Taguchi methods
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