Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.649091
Title: Helmholtz machines and non-stationary data fusion
Author: Dalzell, Ryan
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2001
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Abstract:
This thesis proposes that the autonomous model-building capability of the Helmholtz Machine neural network can be used to reduce the effects of non-stationarity in a data fusion application. The particular application area studied in this work is sensor drift in a sensor array. A solution is attempted by tracking the drift in a subset of the sensors in an array by adapting the neural network's model of the sensor data without affecting the properties of the model. It is shown empirically that the original binary valued unit Helmholtz Machine is suitable for this task in only a limited manner. A new network is therefore introduced: the Discrete valued Helmholtz Machine. Although this network is not found to realise the original proposition it provides valuable new understanding of Helmholtz Machines and their associated Wake-Sleep learning algorithm.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.649091  DOI: Not available
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